DEL

.claude/settings.local.json

@@ -0,0 +1,57 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(mkdir:*)",
+      "Bash(touch:*)",
+      "Bash(python:*)",
+      "Bash(uv run python:*)",
+      "Bash(uv:*)",
+      "WebSearch",
+      "mcp__context7__resolve-library-id",
+      "mcp__context7__get-library-docs",
+      "mcp__tavily-remote-mcp__tavily_search",
+      "Skill(compound-engineering:workflows:review)",
+      "Skill(compound-engineering:file-todos)",
+      "mcp__plugin_compound-engineering_context7__resolve-library-id",
+      "mcp__plugin_compound-engineering_context7__get-library-docs",
+      "Skill(compound-engineering:workflows:work)",
+      "Skill(langgraph-patterns)",
+      "Skill(langgraph-docs)",
+      "Skill(compound-engineering:plan_review)",
+      "mcp__plugin_mongodb-tools_mongodb__connect",
+      "mcp__plugin_paper-search-tools_paper-search__search_arxiv",
+      "Bash(ls:*)",
+      "mcp__plugin_paper-search-tools_paper-search__search_pubmed",
+      "mcp__plugin_paper-search-tools_paper-search__search_semantic",
+      "WebFetch(domain:docs.langchain.com)",
+      "Skill(compound-engineering:create-agent-skills)"
+    ]
+  },
+  "hooks": {
+    "Stop": [
+      {
+        "matcher": "",
+        "hooks": [
+          {
+            "type": "command",
+            "command": "ruff format"
+          },
+          {
+            "type": "command",
+            "command": "ruff check --fix"
+          },
+          {
+            "type": "command",
+            "command": "ty check"
+          }
+        ]
+      }
+    ]
+  },
+  "enabledPlugins": {
+    "claude-mem@thedotmack": true,
+    "paper-search-tools@fcakyon-claude-plugins": true,
+    "mongodb-tools@fcakyon-claude-plugins": true
+  },
+  "outputStyle": "Explanatory"
+}

.claude/skills/langgraph-docs/SKILL.md

@@ -0,0 +1,35 @@
+---
+name: langgraph-docs
+description: Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.
+---
+
+# langgraph-docs
+
+## Overview
+
+This skill explains how to access LangGraph Python documentation to help answer questions and guide implementation. 
+
+## Instructions
+
+### 1. Fetch the Documentation Index
+
+Use the fetch_url tool to read the following URL:
+https://docs.langchain.com/llms.txt
+
+This provides a structured list of all available documentation with descriptions.
+
+### 2. Select Relevant Documentation
+
+Based on the question, identify 2-4 most relevant documentation URLs from the index. Prioritize:
+- Specific how-to guides for implementation questions
+- Core concept pages for understanding questions
+- Tutorials for end-to-end examples
+- Reference docs for API details
+
+### 3. Fetch Selected Documentation
+
+Use the fetch_url tool to read the selected documentation URLs. 
+
+### 4. Provide Accurate Guidance
+
+After reading the documentation, complete the users request.

.claude/skills/langgraph-patterns/SKILL.md

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+---
+name: LangGraph Patterns Expert
+description: Build production-grade agentic workflows with LangGraph using graph-based orchestration, state machines, human-in-the-loop, and advanced control flow
+version: 1.0.0
+---
+
+# LangGraph Patterns Expert Skill
+
+## Purpose
+Master LangGraph for building production-ready AI agents with fine-grained control, checkpointing, streaming, and complex state management.
+
+## Core Philosophy
+
+**LangGraph is:** An orchestration framework with both declarative and imperative APIs focused on control and durability for production agents.
+
+**Not:** High-level abstractions that hide complexity - instead provides building blocks for full control.
+
+**Migration:** LangGraph replaces legacy AgentExecutor - migrate all old code.
+
+## The Six Production Features
+
+1. **Parallelization** - Run multiple nodes concurrently
+2. **Streaming** - Real-time partial outputs
+3. **Checkpointing** - Pause/resume execution
+4. **Human-in-the-Loop** - Approval/correction workflows
+5. **Tracing** - Observability and debugging
+6. **Task Queue** - Asynchronous job processing
+
+## Graph-Based Architecture
+
+```python
+from langgraph.graph import StateGraph, END
+
+# Define state
+class AgentState(TypedDict):
+    messages: Annotated[list, add_messages]
+    next_action: str
+
+# Create graph
+graph = StateGraph(AgentState)
+
+# Add nodes
+graph.add_node("analyze", analyze_node)
+graph.add_node("execute", execute_node)
+graph.add_node("verify", verify_node)
+
+# Define edges
+graph.add_edge("analyze", "execute")
+graph.add_conditional_edges(
+    "execute",
+    should_verify,
+    {"yes": "verify", "no": END}
+)
+
+# Compile
+app = graph.compile()
+```
+
+## Core Patterns
+
+### Pattern 1: Agent with Tools
+```python
+from langgraph.prebuilt import create_react_agent
+
+tools = [search_tool, calculator_tool, db_query_tool]
+
+agent = create_react_agent(
+    model=llm,
+    tools=tools,
+    checkpointer=MemorySaver()
+)
+
+# Run with streaming
+for chunk in agent.stream({"messages": [("user", "Analyze sales data")]}):
+    print(chunk)
+```
+
+### Pattern 2: Multi-Agent Collaboration
+```python
+# Supervisor coordinates specialist agents
+supervisor_graph = StateGraph(SupervisorState)
+
+supervisor_graph.add_node("supervisor", supervisor_node)
+supervisor_graph.add_node("researcher", researcher_agent)
+supervisor_graph.add_node("analyst", analyst_agent)
+supervisor_graph.add_node("writer", writer_agent)
+
+# Supervisor routes to specialists
+supervisor_graph.add_conditional_edges(
+    "supervisor",
+    route_to_agent,
+    {
+        "research": "researcher",
+        "analyze": "analyst",
+        "write": "writer",
+        "finish": END
+    }
+)
+```
+
+### Pattern 3: Human-in-the-Loop
+```python
+from langgraph.checkpoint.sqlite import SqliteSaver
+
+checkpointer = SqliteSaver.from_conn_string("checkpoints.db")
+
+graph = StateGraph(State)
+graph.add_node("propose_action", propose)
+graph.add_node("human_approval", interrupt())  # Pauses here
+graph.add_node("execute_action", execute)
+
+app = graph.compile(checkpointer=checkpointer)
+
+# Run until human input needed
+result = app.invoke(input, config={"configurable": {"thread_id": "123"}})
+
+# Human reviews, then resume
+app.invoke(None, config={"configurable": {"thread_id": "123"}})
+```
+
+## State Management
+
+### Short-Term Memory (Session)
+```python
+class ConversationState(TypedDict):
+    messages: Annotated[list, add_messages]
+    context: dict
+
+checkpointer = MemorySaver()
+app = graph.compile(checkpointer=checkpointer)
+
+# Maintains context across turns
+config = {"configurable": {"thread_id": "user_123"}}
+app.invoke({"messages": [("user", "Hello")]}, config)
+app.invoke({"messages": [("user", "What did I just say?")]}, config)
+```
+
+### Long-Term Memory (Persistent)
+```python
+from langgraph.checkpoint.postgres import PostgresSaver
+
+checkpointer = PostgresSaver.from_conn_string(db_url)
+
+# Persists across sessions
+app = graph.compile(checkpointer=checkpointer)
+```
+
+## Advanced Control Flow
+
+### Conditional Routing
+```python
+def route_next(state):
+    if state["confidence"] > 0.9:
+        return "approve"
+    elif state["confidence"] > 0.5:
+        return "review"
+    else:
+        return "reject"
+
+graph.add_conditional_edges(
+    "classifier",
+    route_next,
+    {
+        "approve": "auto_approve",
+        "review": "human_review",
+        "reject": "reject_node"
+    }
+)
+```
+
+### Cycles and Loops
+```python
+def should_continue(state):
+    if state["iterations"] < 3 and not state["success"]:
+        return "retry"
+    return "finish"
+
+graph.add_conditional_edges(
+    "process",
+    should_continue,
+    {"retry": "process", "finish": END}
+)
+```
+
+### Parallel Execution
+```python
+from langgraph.graph import START
+
+# Fan out to parallel nodes
+graph.add_edge(START, ["agent_a", "agent_b", "agent_c"])
+
+# Fan in to aggregator
+graph.add_edge(["agent_a", "agent_b", "agent_c"], "synthesize")
+```
+
+## Production Deployment
+
+### Streaming for UX
+```python
+async for event in app.astream_events(input, version="v2"):
+    if event["event"] == "on_chat_model_stream":
+        print(event["data"]["chunk"].content, end="")
+```
+
+### Error Handling
+```python
+def error_handler(state):
+    try:
+        return execute_risky_operation(state)
+    except Exception as e:
+        return {"error": str(e), "next": "fallback"}
+
+graph.add_node("risky_op", error_handler)
+graph.add_conditional_edges(
+    "risky_op",
+    lambda s: "fallback" if "error" in s else "success"
+)
+```
+
+### Monitoring with LangSmith
+```python
+import os
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ["LANGCHAIN_API_KEY"] = "..."
+
+# All agent actions automatically logged to LangSmith
+app.invoke(input)
+```
+
+## Best Practices
+
+**DO:**
+✅ Use checkpointing for long-running tasks
+✅ Stream outputs for better UX
+✅ Implement human approval for critical actions
+✅ Use conditional edges for complex routing
+✅ Leverage parallel execution when possible
+✅ Monitor with LangSmith in production
+
+**DON'T:**
+❌ Use AgentExecutor (deprecated)
+❌ Skip error handling on nodes
+❌ Forget to set thread_id for stateful conversations
+❌ Over-complicate graphs unnecessarily
+❌ Ignore memory management for long conversations
+
+## Integration Examples
+
+### With Claude
+```python
+from langchain_anthropic import ChatAnthropic
+
+llm = ChatAnthropic(model="claude-sonnet-4-5")
+agent = create_react_agent(llm, tools)
+```
+
+### With OpenAI
+```python
+from langchain_openai import ChatOpenAI
+
+llm = ChatOpenAI(model="gpt-4o")
+agent = create_react_agent(llm, tools)
+```
+
+### With MCP Servers
+```python
+from langchain_mcp import MCPTool
+
+github_tool = MCPTool.from_server("github-mcp")
+tools = [github_tool, ...]
+agent = create_react_agent(llm, tools)
+```
+
+## Decision Framework
+
+**Use LangGraph when:**
+- Need fine-grained control over agent execution
+- Building complex state machines
+- Require human-in-the-loop workflows
+- Want production-grade durability (checkpointing)
+- Need to support multiple LLM providers
+
+**Use alternatives when:**
+- Want managed platform (use OpenAI AgentKit)
+- Need visual builder (use AgentKit)
+- Want simpler API (use Claude SDK directly)
+- Building on Oracle Cloud only (use Oracle ADK)
+
+## Resources
+
+- Docs: https://langchain-ai.github.io/langgraph/
+- GitHub: https://github.com/langchain-ai/langgraph
+- Tutorials: https://langchain-ai.github.io/langgraph/tutorials/
+
+---
+
+*LangGraph is the production-grade choice for complex agentic workflows requiring maximum control.*

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Context: Bachelor Thesis
+
+**Title:** A Modular Agent Framework for Therapeutic Interview Analysis
+**Goal:** systematically compare local-first/on-premise LLMs against cloud-based state-of-the-art models for a specific therapeutic task (PHQ-8 assessment).
+
+**Core Hypothesis:** Small quantized language models running locally can provide analytical performance comparable to large cloud models when supported by an appropriate agentic framework.
+
+**Key Requirements:**
+-   **Privacy-First:** Architecture must support local/on-premise execution to address clinical data privacy concerns.
+-   **Modularity:** The system must allow easy swapping of underlying models (Tier 1: Local, Tier 2: Self-hosted, Tier 3: Cloud).
+-   **Benchmark:** The system is evaluated on its ability to accurately map therapy transcripts to PHQ-8 (Patient Health Questionnaire) scores using the DAIC-WOZ dataset.
+
+## Commands
+
+-   **Install Dependencies**: `uv sync`
+-   **Run Agent**: `python -m helia.main "Your query here"`
+-   **Lint**: `uv run ruff check .`
+-   **Format**: `uv run ruff format .`
+-   **Type Check**: `uv run ty check`
+-   **Test**: *No test suite currently exists. (Priority Roadmap item)*
+
+## Architecture
+
+Helia is a modular ReAct-style agent framework designed for clinical interview analysis:
+
+1.  **Ingestion** (`src/helia/ingestion/`):
+    -   Parses clinical interview transcripts (e.g., DAIC-WOZ dataset).
+    -   Standardizes raw text/audio into `Utterance` objects.
+
+2.  **Analysis & Enrichment** (`src/helia/analysis/`):
+    -   **MetadataExtractor**: Enriches utterances with sentiment, tone, and speech acts.
+    -   **Model Agnostic**: Designed to swap backend LLMs (OpenAI vs. Local/Quantized models).
+
+3.  **Assessment** (`src/helia/assessment/`):
+    -   Implements clinical logic for standard instruments (e.g., PHQ-8).
+    -   Maps unstructured dialogue to structured clinical scores.
+
+4.  **Persistence Layer** (`src/helia/db.py`):
+    -   **Document-Based Storage**: Uses MongoDB with Beanie (ODM).
+    -   **Core Model**: `AssessmentResult` (in `src/helia/assessment/schema.py`) acts as the single source of truth for experimental results.
+    -   **Data Capture**: Stores the full context of each run:
+        -   **Configuration**: Model version, prompts, temperature (critical for comparing tiers).
+        -   **Evidence**: Specific quotes and reasoning supporting each PHQ-8 score.
+        -   **Outcome**: Final diagnosis and total scores.
+
+5.  **Agent Workflow** (`src/helia/agent/`):
+    -   Built with **LangGraph**.
+    -   **Router Pattern**: Decides when to call specific tools (search, scoring).
+    -   **Tools**: Clinical scoring utilities, Document retrieval.
+
+## Development Standards
+
+-   **Environment**: Requires `OPENAI_API_KEY` and MongoDB credentials.
+-   **Configuration**: managed via Pydantic models in `src/helia/configuration.py`.
+-   **Python**: Uses implicit namespace packages. `__init__.py` files may be missing by design in some subdirectories.
+-   **Code Style**: Follows PEP 8. Enforced via `ruff`.
+-   **Security**: Do not commit secrets. Avoid hardcoding model parameters; use configuration injection to support the comparative benchmark (Tiers 1-3).

THOUGHTS.md

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+- transcript segmenting agent

documents/PHQ8.md

@@ -0,0 +1,364 @@
+ELSEVIER
+
+Journal of Affective Disorders 114 (2009) 163–173
+
+Journal of Affective Disorders
+
+www.elsevier.com/locate/jad
+
+# Research report
+
+# The PHQ-8 as a measure of current depression in the general population
+
+Kurt Kroenke $^{a,\ast}$, Tara W. Strine $^{b}$, Robert L. Spitzer $^{c}$, Janet B.W. Williams $^{c}$, Joyce T. Berry $^{d}$, Ali H. Mokdad $^{b}$
+
+$^{a}$ Department of Medicine, Indiana University School of Medicine and Regenstrief Institute, Indianapolis, IN, United States
+$^{b}$ Centers for Disease Control and Prevention, Atlanta, GA, United States
+$^{c}$ Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, NY, United States
+$^{d}$ Substance Abuse and Mental Health Services Administration, DC, United States
+
+Received 24 October 2007; received in revised form 29 June 2008; accepted 30 June 2008
+
+Available online 27 August 2008
+
+# Abstract
+
+Background: The eight-item Patient Health Questionnaire depression scale (PHQ-8) is established as a valid diagnostic and severity measure for depressive disorders in large clinical studies. Our objectives were to assess the PHQ-8 as a depression measure in a large, epidemiological population-based study, and to determine the comparability of depression as defined by the PHQ-8 diagnostic algorithm vs. a PHQ-8 cutpoint $\geq 10$.
+
+Methods: Random-digit-dialed telephone survey of 198,678 participants in the 2006 Behavioral Risk Factor Surveillance Survey (BRFSS), a population-based survey in the United States. Current depression as defined by either the DSM-IV based diagnostic algorithm (i.e., major depressive or other depressive disorder) of the PHQ-8 or a PHQ-8 score $\geq 10$; respondent sociodemographic characteristics; number of days of impairment in the past 30 days in multiple domains of health-related quality of life (HRQoL).
+
+Results: The prevalence of current depression was similar whether defined by the diagnostic algorithm or a PHQ-8 score $\geq 10$ (9.1% vs. 8.6%). Depressed patients had substantially more days of impairment across multiple domains of HRQoL, and the impairment was nearly identical in depressed groups defined by either method. Of the 17,040 respondents with a PHQ-8 score $\geq 10$, major depressive disorder was present in 49.7%, other depressive disorder in 23.9%, depressed mood or anhedonia in another 22.8%, and no evidence of depressive disorder or depressive symptoms in only 3.5%.
+
+Limitations: The PHQ-8 diagnostic algorithm rather than an independent structured psychiatric interview was used as the criterion standard.
+
+Conclusions: The PHQ-8 is a useful depression measure for population-based studies, and either its diagnostic algorithm or a cutpoint $\geq 10$ can be used for defining current depression.
+
+© 2008 Elsevier B.V. All rights reserved.
+
+Keywords: Depression; Psychometrics; Prevalence; Epidemiology; Quality of life; Patient Health Questionnaire
+
+0165-0327/$ - see front matter © 2008 Elsevier B.V. All rights reserved.
+
+doi:10.1016/j.jad.2008.06.026
+
+# Introduction
+
+Depression is not only the most common mental disorder in general practice as well as mental health settings, but also is a major public health problem. The World Health Organization now recognizes depression as one of the most burdensome diseases in the world (World Health Organization, 2002). It is also among the leading causes of decreased work productivity (Stewart et al. 2003). The prevalence and impact of depression in the United States has been assessed in important population-based studies, with modern methods first used in the Epidemiological Catchment Area study in the early 1980s (Robins and Regier, 1991) and proceeding to the National Comorbidity Survey in 1990--1992 (Kessler et al. 1994) and its replication (NCS-R) a decade later (Kessler et al. 2003). Utilizing structured psychiatric interviews, these landmark epidemiological studies have provided invaluable information on the community prevalence of depression and other mental disorders.
+
+However, there are a number of periodic population-based surveys conducted by federal or state agencies that provide an opportunity for more regular surveillance, although these surveys do not focus exclusively on depression or psychiatric conditions. Because mental health may be only one of a number of health indicators assessed, brief measures may be essential to reduce respondent burden. One increasingly popular measure for assessing depression is the Patient Health Questionnaire nine-item depression scale (PHQ-9). Since its original validation study in 2001 (Kroenke et al. 2001), the PHQ-9 already has been used in several hundred published studies and translated into more than 30 languages. It consists of the nine criteria for depression from the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV). The PHQ-9 is half the length of many depression measures, comparable or superior in operating characteristics, and valid as both a diagnostic and severity measure (Lowe et al. 2004a, Williams et al. 2002a, Williams et al. 2002b). It has been used in clinical (Diez-Quevedo et al. 2001, Kroenke and Spitzer, 2002) and population-based settings (Martin et al. 2006) and is valid in self-administered (Diez-Quevedo et al. 2001, Kroenke et al. 2001) and telephone-administered modes (Pinto-Meza et al. 2005). Additionally, the PHQ-9 is effective for detecting depressive symptoms in various racial/ethnic groups (Huang et al. 2006a, Huang et al. 2006b) and older populations (Klapow et al. 2002), as well as in patients with neurological disorders (Bombardier et al. 2006, Bombardier et al. 2004, Callahan et al. 2006, Fann et al. 2005, Williams et al. 2004, Williams et al. 2005), cardiovascular disease (Holzapfel et al. 2007, Ruo et al. 2003), HIV/AIDS (Justice et al. 2004), diabetes (Glasgow et al. 2004, Katon et al. 2004), chronic kidney disease (Drayer et al. 2006), cancer (Dwight-Johnson et al. 2005), rheumatological disorders (Lowe et al. 2004c, Rosemann et al. 2007), gastrointestinal disease (Persoons et al. 2001), dermatological disorders (Picardi et al. 2004), and other conditions (Lowe et al. 2004b, Maizels et al. 2006, Persoons et al. 2003, Scholle et al. 2003, Spitzer et al. 2000, Tietjen et al. 2007, Turner and Dworkin, 2004, Turvey et al. 2007).
+
+In order to assess the current prevalence and impact of depression in the United States, an eight-item version of the Patient Health Questionnaire depression scale (PHQ-8) recently was made available for use by state health departments in the 2006 Behavioral Risk Factor Surveillance Survey (BRFSS). The PHQ-8 is comparable to the PHQ-9 in terms of diagnosing depressive disorders when using a DSM-IV based diagnostic algorithm (Corson et al. 2004, Kroenke and Spitzer, 2002). However, there is evidence that a PHQ-8 score ≥10 represents clinically significant depression (Kroenke et al. 2001) and is more convenient to use than a diagnostic algorithm. In this paper, we compare the standard diagnostic algorithm and the PHQ-8 cutpoint of 10 in terms of depression prevalence, respondent sociodemographic characteristics, PHQ-8 operating characteristics, and construct validity as assessed by multiple domains of health-related quality of life. Assessment of the PHQ-8 in this large, epidemiological study may provide further evidence of its utility as a depression measure in population-based research.
+
+## Methods
+
+### Behavioral Risk Factor Surveillance Survey (BRFSS)
+
+The BRFSS is a surveillance system operated by state health departments in collaboration with CDC. It aims to collect uniform, state-specific data on preventive health practices and risk behaviors that are linked to chronic diseases, injuries, and preventable infectious diseases in the adult population (Centers for Disease Control and Prevention, 2005, Mokdad et al. 2003). Trained interviewers collect data from a standardized questionnaire using an independent probability sample of households with telephones in the non-institutionalized U.S. adult population. Data from all states and areas were pooled to produce national estimates.
+
+The BRFSS questionnaire consists of three parts: 1) core questions asked in all 50 states, the District of Columbia (D.C.), Puerto Rico (PR), and the U.S. Virgin Islands (USVI); 2) supplemental modules, which are
+
+series of questions on specific topics (e.g. adult asthma history, intimate partner violence, mental health); and 3) state-added questions. In 2006, trained interviewers administered questions about depression severity and lifetime diagnosis of anxiety and depression (Anxiety and Depression Module) in 38 states as well as D.C. PR, and USVI. Additional BRFSS methodology is described elsewhere (Holtzman, 2004). All BRFSS questionnaires, data, and reports are available at http://www.cdc.gov/brfss.
+
+### Patient Health Questionnaire eight-item depression scale (PHQ-8)
+
+To assess the prevalence of depression and its severity in the general U.S. population, the standardized and validated PHQ-8 (see Appendix A) was used (Kroenke and Spitzer, 2002). The PHQ-8 consists of eight of the nine criteria on which the DSM-IV diagnosis of depressive disorders is based (American Psychiatric Association, 1994). The ninth question in the DSM-IV assesses suicidal or self-injurious thoughts. It was omitted because interviewers are not able to provide adequate intervention by telephone. Research indicates that the deletion of this question has only a minor effect on scoring because thoughts of self-harm are fairly uncommon in the general population, and the ninth item is by far the least frequently endorsed item on the PHQ-9 (Huang et al. 2006a, Kroenke and Spitzer, 2002, Lee et al. 2007, Rief et al. 2004). Indeed, the two original validation studies of the PHQ totaling 6000 patients established that identical scoring thresholds for depression severity could be used for the PHQ-9 and PHQ-8 (Kroenke and Spitzer, 2002).
+
+The PHQ-8 response set was standardized to make it similar to other BRFSS questions by asking the number of days in the past 2 weeks the respondent had experienced a particular depressive symptom. The modified response set was converted back to the original response set: 0 to 1 day=“not at all,” 2 to 6 days=“several days,” 7 to 11 days=“more than half the days,” and 12 to 14 days=“nearly every day,” with points (0 to 3) assigned to each category, respectively. The scores for each item are summed to produce a total score between 0 and 24 points. A total score of 0 to 4 represents no significant depressive symptoms. A total score of 5 to 9 represents mild depressive symptoms; 10 to 14, moderate; 15 to 19, moderately severe; and 20 to 24, severe. (Kroenke et al. 2001) Current depression was defined in two ways: 1) a PHQ-8 algorithm diagnosis of major depression (this requires either the first or second item (depressed mood or anhedonia) to be present “more than half the days” and at least 5 of the 8 symptoms to be present “more than half the days”) or other depression (2 to 4 symptoms, including depressed mood or anhedonia, are required to be present “more than half the days”); 2) a PHQ-8 score of ≥10, which has an 88% sensitivity and 88% specificity for major depression (Kroenke and Spitzer, 2002) and, regardless of diagnostic status, typically represents clinically significant depression (Corson et al. 2004, Kroenke et al. 2001).
+
+### Health-related quality of life and other items
+
+Three health-related quality of life (HRQoL) questions with demonstrated validity and reliability for population health surveillance were examined (Andresen et al. 2003, Mielenz et al. 2006, Moriarty et al. 2003). The three questions involved respondents' self-assessment of their health over the previous 30 days. 1) Physical health: “How many days was your physical health, which includes physical illness or injury, not good?” 2) Mental health: “How many days was your mental health, which includes stress, depression, and problems with emotions, not good?” 3) Activity limitations: “How many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?”
+
+Additionally, a “Healthy Days Symptoms Module” was used in Delaware, Hawaii, and Rhode Island. Questions in this module also referred to the previous 30 days: 1) Depressive symptoms: “How many days did you feel sad, blue, or depressed?” 2) Anxiety symptoms: “How many days did you feel worried, tense, or anxious?” 3) Sleep problems: “How many days have you felt you did not get enough rest or sleep?” 4) Pain limitations: “How many days did pain make it difficult to do your usual activities?” 5) Vitality: “How many days have you felt very healthy and full of energy?” We calculated fatigue by subtracting the number of days of vitality from 30.
+
+Sociodemographic information was obtained for each respondent. Employment status was assessed by the question: “Are you currently: employed for wages, self-employed, out of work for more than 1 year, out of work for less than 1 year, a homemaker, a student, retired, or unable to work?” Additionally, two questions were asked about lifetime diagnosis: “Has a doctor or other health care provider ever told you that you have an anxiety disorder (including acute stress disorder, anxiety, generalized anxiety disorder, obsessive-compulsive disorder, panic attacks, panic disorder, phobia, posttraumatic stress disorder, or social anxiety disorder)?” and “Has a doctor or other health care provider ever told you that you have a depressive disorder (including depression, major depression, dysthymia, or minor depression)?”
+
+There were 198,678 respondents from the 38 states, D.C. PR, and USVI who completed all PHQ-8
+
+questions. Of these, nearly all (198,574, or 99.95%) completed at least one of the first three HRQoL items (196,673 for mental health, 196,141 for physical health, and 197,543 for activity limitations). Among the 13,622 respondents in Delaware, Hawaii, and Rhode Island, 13,619 (99.98%) answered at least one of the 5 HRQOL questions (depression 13,514, anxiety 13,487, sleep 13,536, fatigue 13,381, and pain 13,534). The median cooperation rate of BRFSS (i.e. the percentage of eligible respondents who completed the survey) was 74.5%.
+
+### Analysis
+
+Depression was classified as either major depression or other depression (using the PHQ-8 diagnostic algorithm as described) or a PHQ-8 score ≥10. Sociodemographic characteristics of depressed and nondepressed respondents were compared. The frequency distribution of major depression and other depression by standard PHQ-8 severity intervals (0--4, 5--9, 10--14, 15--19, and 20--24) as well as the commonly used cutpoint of ≥10 were described (Kroenke and Spitzer, 2002). Operating characteristics (sensitivity, specificity, likelihood ratios) for PHQ-8 intervals and cutpoint were calculated, using the PHQ-8 diagnostic algorithm as the criterion standard (Kroenke and Spitzer, 2002, Kroenke et al. 2001). The mean number of impairment days in the past 30 days for HRQoL domains was determined for depressed and nondepressed groups. Because of the large sample size, statistical testing was not emphasized.
+
+Weighting in BRFSS is designed to make the total number of cases equal to the number of people in the state who are age 18 and older. In the BRFSS, such poststratification serves as an adjustment for noncoverage and nonresponse and forces the total number of cases to equal population estimates for each geographic region, which for the BRFSS is usually a state. Sample characteristics (Table 1) and PHQ-8 operating characteristics (Table 2) use unweighted BRFSS data, while construct validity analyses (Table 3 and Fig. 1, Fig. 2) use weighted data.
+
+## Results
+
+### Respondent characteristics
+
+Data were analyzed from 198,678 respondents to the 2006 BRFSS survey. Overall, the sample was 61.6% women, 78% non-Hispanic white, 58.3% currently employed, 61.2% college educated, and 56.9% currently married. A lifetime diagnosis of a depressive or anxiety disorder was reported by 18.0% and 12.3%, respectively.
+
+Table 1 compares the characteristics of depressed vs. nondepressed respondents, with depression defined either by the PHQ-8 diagnostic algorithm (major depressive or other depressive disorder) or by a PHQ-8 cutpoint ≥10. Two findings should be emphasized. First, depressed respondents were more likely to be women, nonwhite, less educated, unemployed or unable to work, unmarried, and younger than 55 years. Not surprisingly, depressed respondents also were much more likely to report lifetime diagnoses of both depression and anxiety. Second, characteristics of the depressed groups were quite similar between the two methods of defining depression, as were characteristics of the nondepressed groups. Compared to the diagnostic algorithm, the cutpoint method produced slightly lower estimates of depression in men and in the two oldest age groups and modestly higher estimates in those with a self-reported lifetime diagnosis of depression or anxiety.
+
+### PHQ-8 distribution and operating characteristics
+
+Table 2 shows the relationship between PHQ-8 severity scores and depression diagnostic status. There were 8476 respondents with major depression in the BRFSS sample using the PHQ-8 diagnostic algorithm, resulting in a prevalence of 4.3%. There were 18,053 respondents with any depression using the diagnostic algorithm and 17,040 with any depression using a PHQ-8 cutpoint of ≥10, yielding relatively similar prevalences of 9.1% and 8.6%, respectively. No respondents with scores less than 10 had major depression, because this diagnosis requires at least 5 symptoms to be present more than half the days (resulting in a score of 2 for each symptom).
+
+The sensitivity of a PHQ-8 score ≥10 is the proportion of respondents with a depressive disorder who have a score of 10 or greater, and the specificity is the proportion of respondents without a depressive disorder who have a score less than 10. The sensitivity and specificity of a PHQ-8 score ≥10 for major depressive disorder (vs. other + none) were 100% (8476/8476) and 95% (181,638)/(190,202), respectively; for any depressive disorder, the sensitivity and specificity were 70% (12,556/18,053) and 98% (176,141/180,625). We also calculated the likelihood ratios associated with the PHQ-8 score ranges or thresholds shown in Table 3. The likelihood ratio is defined as the ratio of the probability of a score range or threshold count in individuals with and without a depressive disorder. For example, 9968 people had PHQ-8 scores of 10--14. The likelihood ratio associated
+
+K. Kroenke et al. / Journal of Affective Disorders 114 (2009) 163-173
+167
+
+Table 1
+Characteristics of respondents by depression status — percent in various groups
+
+|  Characteristic | No depressive disorder (n=180,625) | PHQ-8<10 (n=181,638) | Depressive disorder* (n=18,053) | PHQ-8 ≥10 (n=17,040)  |
+| --- | --- | --- | --- | --- |
+|  Sex |  |  |  |   |
+|  Women | 61.0 | 60.7 | 67.4 | 71.3  |
+|  Men | 39.0 | 39.3 | 32.6 | 28.7  |
+|  Age |  |  |  |   |
+|  18–24 | 4.5 | 4.5 | 5.9 | 6.2  |
+|  25–34 | 12.4 | 12.4 | 12.3 | 13.1  |
+|  35–44 | 17.6 | 17.5 | 18.3 | 19.6  |
+|  45–54 | 21.1 | 21.0 | 24.7 | 26.4  |
+|  55–64 | 19.9 | 19.8 | 20.2 | 20.5  |
+|  65–74 | 14.0 | 14.2 | 10.6 | 8.6  |
+|  75 or greater | 10.5 | 10.7 | 8.0 | 5.6  |
+|  Race/ethnicity |  |  |  |   |
+|  White | 78.8 | 78.6 | 69.6 | 70.9  |
+|  Black | 7.7 | 7.8 | 11.4 | 10.7  |
+|  Hispanic | 7.6 | 7.7 | 11.1 | 10.2  |
+|  Other | 5.9 | 5.9 | 7.9 | 8.1  |
+|  Education |  |  |  |   |
+|  <High school | 8.5 | 8.6 | 19.4 | 18.7  |
+|  High school | 28.6 | 28.7 | 36.6 | 35.5  |
+|  ≥College | 62.9 | 62.7 | 44.0 | 45.8  |
+|  Employment status |  |  |  |   |
+|  Employed | 60.0 | 60.0 | 41.0 | 40.4  |
+|  Retired | 23.0 | 23.3 | 16.4 | 13.1  |
+|  Homemaker or student | 10.0 | 10.0 | 10.0 | 10.3  |
+|  Unable to work | 3.7 | 3.6 | 23.7 | 26.6  |
+|  Unemployed | 3.2 | 3.2 | 9.0 | 9.6  |
+|  Marital status |  |  |  |   |
+|  Currently married | 58.6 | 58.5 | 40.9 | 40.3  |
+|  Previously married | 26.6 | 26.7 | 39.5 | 39.4  |
+|  Never married | 14.9 | 14.8 | 19.6 | 20.3  |
+|  Lifetime diagnoses |  |  |  |   |
+|  Depression | 14.7 | 14.1 | 51.0 | 59.7  |
+|  Anxiety | 9.9 | 9.4 | 36.8 | 43.3  |
+
+* Depressive disorder is major depression or other depression according to PHQ-8 algorithm.
+
+with a score in this range was (5922/18,053) ÷ (4046/180,625) = 15. This means that a PHQ-8 score of 10–14 is 15 times more likely to occur in persons with a depressive disorder than in persons without a depressive disorder. The likelihood ratios for any depressive disorder for PHQ-8 scores of 0–4, 5–9, 10–14, 15–19, and 20–24 were 0.01, 2, 15, 103, and 2237. The likelihood ratio for a PHQ-8 score ≥ 10 was 28.
+
+Some of the DSM-IV criteria for depressive disorders are nonspecific, particularly the somatic symptoms (fatigue, trouble sleeping, and appetite or weight changes). They are frequent physical complaints in the general population, arising not only from depression, but also from a number of medical conditions. They also exist as idiopathic symptoms. These symptoms could contribute to elevated PHQ-8 scores and account for false–positive cases of depression when using a PHQ-8
+
+cut point (≥ 10) rather than the diagnostic algorithm. On the other hand, two DSM-IV criteria – depressed mood and anhedonia – are specific to depressive disorders: At least one of these two symptoms is required for diagnosis. Of the 4484 persons with a PHQ-8 score ≥ 10 who did not meet DSM-IV criteria for either a major or other depressive disorder, 597 (13.3%) scored zero on both depressed mood and anhedonia. The rest scored a 1 or greater on either or both symptoms, meaning that they experienced at least 2–6 days of one or both core depressive symptoms in the past 2 weeks. Of the 17,040 respondents with a PHQ-8 score ≥ 10, major depressive disorder was present in 8476 (49.7%), other depressive disorder in 4080 (23.9%), depressed mood or anhedonia in 3887 (22.8%), and no evidence of depressive disorder or depressive symptoms in 597 (3.5%).
+
+K. Kroenke et al. / Journal of Affective Disorders 114 (2009) 163-173
+
+Table 2 Distribution of depressive symptom severity and depressive disorders in BRFSS
+
+|  Depressive symptom severity [PHQ-8 Score] | Depressive disorder by diagnostic algorithm |   |   |   | Total  |
+| --- | --- | --- | --- | --- | --- |
+|   |  Major | Other | Any | None  |   |
+|   |  (n=8476) | (n=9577) | (n=18,053) | (n=180,625) | (n=198,678)  |
+|  0–4 | 0 | 200 | 200 | 149,921 | 150,121  |
+|  5–9 | 0 | 5297 | 5297 | 26,220 | 31,517  |
+|  10–14 | 1944 | 3978 | 5922 | 4046 | 9968  |
+|  15–19 | 4296 | 102 | 4398 | 428 | 4826  |
+|  20–24 | 2236 | 0 | 2236 | 10 | 2246  |
+|  ≥10 | 8476 | 4080 | 12,556 | 4484 | 17,040  |
+|  <10 | 0 | 5497 | 5497 | 176,141 | 181,638  |
+
+## 3.3. HRQoL impairment and depression
+
+Table 3 summarizes the number of days in the past 30 days that respondents reported impairment in various domains of HRQoL. There are several key findings. First, compared to nondepressed respondents, those who are depressed reported substantially more days of impairment in all domains of HRQoL. Not surprisingly, depressed patients experienced the greatest impairment in depressive and anxiety symptoms, somatic symptoms that are core criteria for depression (fatigue and sleep problems), and mental health, reporting 10–20 days of impairment in each of these domains in the past 30 days. They also reported limitations during 10 of the past 30 days due to physical problems or pain.
+
+A second key finding is that the number of days of impairment in both depressed and nondepressed individuals does not differ by definition (PHQ-8 diagnostic algorithm vs. a PHQ-8 cutpoint of 10). For every domain, impairment is equal or slightly greater for depression defined by a PHQ-8 score $\geq 10$ (Figs. 1 and 2).
+
+## 4. Discussion
+
+BRFSS provided an excellent opportunity to examine the PHQ-8 in a large, representative study of the U.S. population. The two methods of estimating current depression – the validated PHQ-8 diagnostic algorithm based upon DSM-IV criteria, and a PHQ-8 cutpoint of 10 – yielded similar prevalences (9.1% and 8.6%, respectively). Also, sociodemographic characteristics were similar in the depressed and nondepressed groups defined by these two methods. Patients classified by the PHQ-8 as having current depression had substantial impairment across multiple domains of HRQoL, and impairment was nearly identical whether groups were classified by the diagnostic algorithm or cutpoint approach. Finally, 96.5% of respondents with a PHQ-8 score $\geq 10$ had either a depressive disorder or core depressive symptoms.
+
+The population prevalence of current depression detected by the PHQ-8 in BRFSS is similar to those reported in some other population-based studies, and slightly lower than those reported in other studies. The
+
+Table 3 Relationship between impairment in health-related quality of life and depression status as determined by PHQ-8 diagnostic algorithm and cutpoint
+
+|  Health-related quality of life domain | Mean number (SEM) of impaired days in past 30 days in specific health-related quality of life domains  |   |   |   |
+| --- | --- | --- | --- | --- |
+|   |  No depressive disorder1 | PHQ-8 <10 | Depressive disorder1 | PHQ-8 ≥10  |
+|  Physical health not good | 2.78 (0.04) | 2.75 (0.04) | 10.09 (0.19) | 10.76 (0.20)  |
+|  Mental health not good | 2.27 (0.03) | 2.23 (0.03) | 14.37 (0.20) | 15.34 (0.21)  |
+|  Activity limited days | 1.39 (0.03) | 1.36 (0.03) | 9.49 (0.18) | 10.23 (0.19)  |
+|  Depressive symptoms**2** | 1.44 (0.05) | 1.43 (0.04) | 13.70 (0.52) | 15.57 (0.51)  |
+|  Anxiety symptoms**2** | 3.24 (0.08) | 3.21 (0.09) | 15.15 (0.55) | 17.25 (0.55)  |
+|  Fatigue**2** | 9.33 (0.12) | 9.30 (0.12) | 20.98 (0.50) | 23.13 (0.36)  |
+|  Sleep problems**2** | 6.98 (0.12) | 6.93 (0.12) | 17.04 (0.52) | 19.17 (0.51)  |
+|  Pain-limited days**2** | 2.14 (0.07) | 2.14 (0.07) | 9.16 (0.46) | 10.19 (0.51)  |
+
+a Depressive disorder is major depression or other depression according to PHQ-8 algorithm.
+b Questions asked only in Delaware, Hawaii, and Rhode Island.
+
+K. Kroenke et al. / Journal of Affective Disorders 114 (2009) 163-173
+
+![img-0.jpeg](img-0.jpeg)
+Fig. 1. Number of self-reported impairment days out of the past 30 days in the domains of mental health, physical health, and activity limitations. Data available from 198,678 BRFSS respondents from 38 states, the District of Columbia, Puerto Rico, and U.S. Virgin Islands.
+
+National Comorbidity Survey in the early 1990s, and its replication a decade later, revealed that the 12-month prevalence of a major depressive episode in the U.S. population ranges from 6.6% to 10.3% (Kessler et al., 1994; Kessler et al., 2003). A population-based study in Germany using the PHQ-9 reported major depression in 3.8% and other depression in 5.4% of respondents (vs. 4.3% and 4.8% in the BRFSS sample) (Martin et al., 2006). The same study reported that 7.2% of respondents had a PHQ-9 score ≥ 10, vs. 8.6% of BRFSS respondents (Rief et al., 2004). A population-based study in Australia using the PRIME-MD (an earlier interviewer-administered version of the PHQ) found a 7% prevalence of major depression and an 18% prevalence of any depression (Hawthorne et al., 2003). Our BRFSS findings of current depression by the PHQ-8 diagnostic algorithm (9.1%) or cutpoint of 10 (8.6%) suggest that rates using the PHQ-8 probably represent accurate or slightly conservative estimates of depression prevalence in the United States.
+
+There are several large studies confirming the validity of the PHQ-8 as both a diagnostic and severity measure in clinical populations (Corson et al., 2004; Kroenke and Spitzer, 2002). Our study is the first to test the PHQ-8 in a large population-based survey. The PHQ-8 may be useful in some types of research, particularly epidemiological/population-based studies, postal or web-based surveys, and clinical studies in which depression is a secondary outcome. In such instances, depression prevalence and severity are expected to be low, the 9th item of the PHQ-9 is infrequently endorsed and even then usually represents passive thoughts of death rather than suicidal ideation, and immediate mental health back-up to interview all the false-positive endorsements of the 9th item is not feasible. In contrast, the PHQ-9 would be preferred in most settings where depression is being diagnosed and
+
+treated because of the importance of recognizing those uncommon but serious instances of suicidal ideation. Also, a modeling study of general practitioners' decision-making suggested that potential suicidal ideation was one of the two most important cues (the other being duration of depressive symptoms) for prescribing antidepressants (Smith and Gilhooly, 2006).
+
+The comparison in BRFSS of two different methods of scoring the PHQ-8 also has broader implications regarding the current debate about a categorical vs. dimensional approach to psychiatric diagnoses. In fact, it is likely that revisions in DSM-V (and possibly ICD 11) will emphasize the complementary value of adding dimensional assessment to the categorical diagnoses of depression as well as other psychiatric disorders (Helzer et al., 2006). Likewise, Kendall and Jablensky argue that while discrete boundaries within and between psychiatric diagnoses have several limitations in terms of validity, coupling dimensional assessment with categorical diagnoses may have utility in monitoring response to treatment, predicting outcomes (e.g., recovery, remission, relapse), comparing different populations, evaluating the impact of social and occupational functioning, and investigating potential etiology (Kendall and Jablensky, 2003). The categorical and dimensional scoring of the PHQ-9 and PHQ-8 may therefore be advantageous.
+
+Systematic reviews have shown that the PHQ-9 has operating characteristics comparable to other depression measures, including the Hospital Anxiety and Depression (HADS) scale, in terms of depression screening and case-finding (Williams et al., 2002a,b). While the 7-item depression subscale of the HADS excludes somatic symptoms that may be confounded by comorbid medical disorders, a meta-analysis of the PHQ-9 showed good operating characteristics across a range of medical
+
+![img-1.jpeg](img-1.jpeg)
+Fig. 2. Number of self-reported days in the past 30 days with depressive symptoms, anxiety symptoms, fatigue, sleep problems, and pain limiting activity. Data available from 13,622 BRFSS respondents in 3 states: Delaware, Hawaii, and Rhode Island.
+
+K. Kroenke et al. / Journal of Affective Disorders 114 (2009) 163-173
+
+disorders (Gilbody et al., 2007b). Also, there is not strong empiric data to support the exclusion of somatic symptoms in diagnosing depression in medically ill populations (Koenig et al., 1997). A treatment study in 32 general practices in Scotland found that the PHQ-9 and HADS had similar reliability, discriminant and factorial validity, and sensitivity to change (Cameron et al., 2008). However, the PHQ-9 classified more patients with moderate/severe depression, prompting the authors to conclude that further work is needed to assess the validity of the two scales' severity cut-off bands. A recent validation study in the UK found the PHQ-9 as good as clinician-administered instruments in detecting depression in primary care (Gilbody et al., 2007a).
+
+BRFSS made one important modification to the PHQ-8: respondents were asked to report the number of days they were bothered by each depressive symptom in the past 2 weeks, and then this number was converted to the standard verbal response options of the PHQ-8. These conversions had substantial face validity: 0–1 days is less than “several days” and 7–11 days is “more than half the days.” In addition, the training manual for the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID) instructs the interviewer that more than 11 days is the cutpoint if respondents insist on explicit guidance for what is meant by “nearly every day in the past 2 weeks” (First et al., 1996). Ideally, the two methods of administering and scoring the PHQ-8 would be tested in a single sample using a structured psychiatric interview as the criterion standard. However, the fact that the population prevalence of current depression detected by the PHQ-8 using the “number of days” response set is comparable to previous epidemiological studies, combined with the strong construct validity demonstrated by our HRQoL findings, provides important preliminary validation data for the “number of days” approach. Of note, a response set inquiring about number of days in the past week is also being tested in several studies using interactive voice recorded PHQ-9 administration. In these studies, 0–1 day is scored as 0 (“not at all”), 2–3 days as 1 (“several days”), 4–5 days as 2 (“more than half the days”), and 6–7 days as 3 (“nearly every day”).
+
+Several limitations of our study must be acknowledged. First, BRFSS excludes individuals in institutions and those who cannot participate in a telephone interview (severe mental illness or cognitive impairment, lack of a phone due to low income, etc.). Because these groups may have higher rates of depression, BRFSS may slightly underestimate the true prevalence. Second, although our prevalence and HRQoL impairment data provide strong preliminary evidence for the “number of days” response set as a valid alternative to the standard PHQ response set,
+
+this finding would be strengthened by testing both response sets against a criterion standard in a single sample. Third, despite the similar prevalences, respondent sociodemographics, and HRQoL impairment of current depression as defined by the diagnostic algorithm and the cutpoint approach, there was a moderate degree of non-overlap in the actual groups identified by the two methods. Treatment and other types of longitudinal studies should further compare the prognosis and outcomes of depression defined by the PHQ in these two ways. Fourth, even if a cutpoint approach may suffice for large, epidemiological studies, further inquiry is advisable in the clinical care of individual patients. For example, a PHQ-8 score of 10 or greater in the clinical setting should prompt a careful interview for a number of reasons: to determine if the elevated score represents clinical depression, to consider confounding medical causes, to evaluate for affective disorders that may warrant either more conservative initial treatment than major depression (e.g., minor depression or bereavement) or earlier mental health referral (e.g., bipolar disorder or comorbid substance abuse), and to avoid premature labeling of a person as having depression.
+
+There are a number of depression instruments – varying in length from 2 to 20 or more items – that are generally comparable as measures of potential caseness for the purposes of depression screening (Mitchell and Coyne, 2007; Williams et al., 2002a,b). Moreover, these instruments have similar degrees of association with measures of functional status, disability, and HRQoL (Fleishman and Zuvekas, 2007). Compared with many of these, the PHQ-8 (like the PHQ-9) is equally good as a measure of severity and is superior as a diagnostic measure for DSM-IV depressive disorders. Our data from a representative sample of nearly 200,000 individuals in the United States supports the value of the PHQ-8 in population studies.
+
+## Role of funding source
+
+There was no external funding for this study.
+
+## Conflict of interest
+
+The authors have no conflicts of interest with respect to this paper.
+
+## Acknowledgments
+
+We thank the state health department personnel who collaborated with in the Centers for Disease Control and Prevention (CDC) and the Substance Abuse and Mental Health Services Administration (SAMHSA) on the implementation of the Anxiety and Depression Module for the Behavioral Risk Factor Surveillance System (BRFSS).
+
+K. Kroenke et al. / Journal of Affective Disorders 114 (2009) 163-173
+
+# Appendix A: Patient Health Questionnaire eight-item depression measure (PHQ-8)
+
+PHQ-8
+
+|  Over the last 2 weeks, how often have you been bothered by any of the following problems?  |   |   |   |   |
+| --- | --- | --- | --- | --- |
+|  (Use “✓” to indicate your answer) | Not at all | Several days | More than half the days | Nearly every day  |
+|  1. Little interest or pleasure in doing things | 0 | 1 | 2 | 3  |
+|  2. Feeling down, depressed, or hopeless | 0 | 1 | 2 | 3  |
+|  3. Trouble falling or staying asleep, or sleeping too much | 0 | 1 | 2 | 3  |
+|  4. Feeling tired or having little energy | 0 | 1 | 2 | 3  |
+|  5. Poor appetite or overeating | 0 | 1 | 2 | 3  |
+|  6. Feeling bad about yourself – or that you are a failure or have let yourself or your family down | 0 | 1 | 2 | 3  |
+|  7. Trouble concentrating on things, such as reading the newspaper or watching television. | 0 | 1 | 2 | 3  |
+|  8. Moving or speaking so slowly that other people could have noticed? Or the opposite – being so fidgety or restless that you have been moving around a lot more than usual | 0 | 1 | 2 | 3  |
+|  (For office coding: Total Score —— = —— + —— + ——)  |   |   |   |   |
+
+From the Primary Care Evaluation of Mental Disorders Patient Health Questionnaire (PRIME-MDPHQ). The PHQ was developed by Drs. Robert L. Spitzer, Janet B.W. Williams, Kurt Kroenke and colleagues. For research in formation, contact Dr. Spitzera trls8@columbia.edu. PRIME-MD® is a trademark of Pfizer Inc. Copyright © 1999 Pfizer Inc. All rights reserved. Reproduced with permission
+
+# References
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+
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+
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+
+First, M.B., Spitzer, R.L., Gibbon, M., Williams, J.B., 1996. Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). American Psychiatric Press Inc.
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+
+Helzer, J.E., Kraemer, H.C., Krueger, R.F., 2006. The feasibility and need for dimensional psychiatric diagnoses. Psychol. Med. 36, 1671–1680.
+
+Holtzman, D., 2004. The Behavioral Risk Factor Surveillance System. In: Blumenthal, D.S., DiClemente, R.J. (Eds.), Community-based Health Research Issues and Methods. Springer, New York, pp. 115–131.
+
+Holzapfel, N., Zugck, C., Muller-Tasch, T., Lowe, B., Wild, B., Schellberg, D., Nelles, M., Herzog, W., Junger, J., 2007. Routine screening for depression and quality of life in outpatients with congestive heart failure. Psychosomatics 48 (2), 112–116.
+
+Huang, F.Y., Chung, H., Kroenke, K., Delucchi, K.L., Spitzer, R.L., 2006a. Using the Patient Health Questionnaire-9 to measure depression among racially and ethnically diverse primary care patients. J. Gen. Intern. Med. 21 (6), 547–552.
+
+Huang, F.Y., Chung, H., Kroenke, K., Spitzer, R.L., 2006b. Racial and ethnic differences in the relationship between depression severity and functional status. Psychiatr. Serv. 57 (4), 498–503.
+
+Justice, A.C., McGinnis, K.A., Atkinson, J.H., Heaton, R.K., Young, C., Sadek, J., Madenwald, T., Becker, J.T., Conigliaro, J., Brown, S.T., Rimland, D., Crystal, S., Simberkoff, M., 2004. Psychiatric and neurocognitive disorders among HIV-positive and negative veterans in care: Veterans Aging Cohort Five-Site Study. AIDS 18, S49–S59.
+
+Katon, W.J., Simon, G., Russo, J., Von Korff, M., Lin, E.H.B., Ludman, E., Ciechanowski, P., Bush, T., 2004. Quality of depression care in a population-based sample of patients with diabetes and major depression. Med. Care 42 (12), 1222–1229.
+
+Kendall, R., Jablensky, A., 2003. Distinguishing between the validity and utility of psychiatric diagnoses. Am. J. Psychiatry 160, 4–12.
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+Kessler, R.C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K.R., Rush, A.J., Walters, E.E., Wang, P.S., 2003. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 289 (23), 3095–3105.
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+Kessler, R.C., McGonagle, K.A., Zhao, S., Nelson, C.B., Hughes, M., Eshelman, S., Wittchen, H., Kendler, K.S., 1994. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Arch. Gen. Psychiatry 51 (1), 8–19.
+
+Klapow, J., Kroenke, K., Horton, T., Schmidt, S., Spitzer, R., Williams, J.B., 2002. Psychological disorders and distress in older primary care patients: a comparison of older and younger samples. Psychosom. Med. 64 (4), 635–643.
+
+Koenig, H.G., George, L.K., Peterson, B.L., Pieper, C.F., 1997. Depression in medically ill hospitalized older adults: prevalence, characteristics, and course of symptoms according to six diagnostic schemes. Am. J. Psychiatry 154, 1376–1383.
+
+Kroenke, K., Spitzer, R.L., 2002. The PHQ-9: a new depression and diagnostic severity measure. Psychiatric Ann. 32, 509–521.
+
+Kroenke, K., Spitzer, R.L., Williams, J.B.W., 2001. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613.
+
+Lee, P.W., Schulberg, H.C., Raue, P.J., Kroenke, K., 2007. Concordance between the PHQ-9 and the HSCL-20 in depressed primary care patients. J Affect. Disord. 99 (1–3), 139–145.
+
+Lowe, B., Kroenke, K., Herzog, W., Grafe, K., 2004a. Measuring depression outcome with a brief self-report instrument: sensitivity to change of the Patient Health Questionnaire (PHQ-9). J Affect. Disord. 81 (1), 61–66.
+
+Lowe, B., Unutzer, J., Callahan, C.M., Perkins, A.J., Kroenke, K., 2004b. Monitoring depression treatment outcomes with the Patient Health Questionnaire-9. Med. Care 42 (12), 1194–1201.
+
+Lowe, B., Willand, L., Eich, W., Zipfel, S., Ho, A.D., Herzog, W., Fiehn, C., 2004c. Psychiatric comorbidity and work disability in patients with inflammatory rheumatic diseases. Psychosom. Med. 66 (3), 395–402.
+
+Maizels, M., Smitherman, T.A., Penzien, D.B., 2006. A review of screening tools for psychiatric comorbidity in headache patients. Headache 46, S98–S109.
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+
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+
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+
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+World Health Organization, 2002. The World Health Report 2002: Reducing Risks, Promoting Healthy Life. World Health Organization, Geneva, Switzerland.

documents/bt-thesis-outline-sma.md

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+# **Bachelor Thesis Exposé \- Santiago Martinez-Avial**
+
+A Modular Agent Framework for Therapeutic Interview Analysis: Comparing Local, Self-Hosted, and Cloud LLM Deployments
+
+**Supervisor / First Examiner:** Peter Ruppel
+
+**Second Examiner:** Adam Roe
+
+---
+
+## **Introduction**
+
+This thesis aims to bridge the fields of Artificial Intelligence and mental healthcare, attempting to enhance a therapist’s workflow rather than replace it. When performing Diagnostic interviews, therapists often rely on standardized questionnaires or forms. These conversations can be long, detailed, and information-dense, and much of the follow-up work involves organizing what was said into formal assessments. An Agentic AI system could support therapists by recording and transcribing sessions, extracting and structuring relevant information, and mapping it onto these questionnaires. It could then offer a second opinion or preliminary analysis that may highlight patterns or insights the therapist might want to explore further.
+
+However, the implementation of such systems proves difficult: most state-of-the-art models are only available through inference APIs on the cloud, which creates legal and regulatory challenges. Additionally both patients and therapists are often uneasy about highly personal conversations being transmitted, stored, and processed on remote servers over which they have virtually no control.
+
+This thesis is motivated by that conflict. Instead of simply accepting a trade-off between “privacy or performance,” the thesis aims to systematically test how far a local-first or on-premise architecture can close the gap to state-of-the-art cloud systems on a concrete therapeutic analysis task.
+
+My working hypothesis is: Given an appropriate supporting framework, small quantized language models running locally or on‑premise can provide analytical performance comparable to large cloud-based state-of-the-art models for a specific therapeutic analysis task.
+
+If confirmed, this hypothesis could lay the groundwork for wider adoption of AI in therapy and other areas where data sensitivity is critical.
+
+## **Approach and preliminary results**
+
+To evaluate this hypothesis, I will design, implement, and benchmark a modular software system against a formal specification derived from a realistic clinical use case, based on publicly available datasets. All engineering choices will be tied to a specific analytical task rather than to abstract model comparisons. This yields both a meaningful benchmark and a clinically relevant evaluation setup.
+
+The work is organized into the following components:
+
+### **Benchmark framework and dataset selection**
+
+A useful comparison between local, self-hosted and cloud-based models requires a benchmark that is both clinically grounded and empirically measurable. For this project, that means basing the evaluation on established questionnaires or rating scales that use standardized scoring procedures that can serve as “ground truth,” and are supported by relevant use in clinical or research settings.
+
+Unfortunately, the biggest limiting factor is a lack of access to relevant material: many widely used tools and datasets are either paywalled or restricted to certain institutions. Given these constraints, I have identified three promising options so far:
+
+* NCS-R Interviews (National Comorbidity Survey): a set of structured questionnaires used to assess mental health and diagnose disorders  
+* Checklist of Cognitive Distortions: a questionnaire used to screen for and measure the severity of depressive symptoms  
+* PHQ-8 (Patient Health Questionnaire-8): an eight-item questionnaire used to screen for and measure the severity of depressive symptoms in the general population
+
+The PHQ-8 will likely be the selected tool, as I have recently been granted access to the DAIC-WOZ database, which contains audio recordings and transcripts of semi-structured clinical interviews and associated PHQ-8 scores for each participant.
+
+This combination of real-world data and standardized questionnaire labels makes DAIC-WOZ particularly well suited as the primary benchmark dataset.
+
+If I am unable to obtain access to comparable real interview datasets for other instruments that cannot be obtained, I will generate synthetic interviews from questionnaires based on proven methods such as those described in [this paper](https://arxiv.org/html/2510.25384v1) which propose generating therapist–client dialogues based on questionnaire items and responses. This would allow me to construct controlled benchmarks when real data are unavailable, while keeping the evaluation grounded in established academic instruments.
+
+### 
+
+### 
+
+### **Software system: a ReAct-style agent**
+
+The main technical component will be a modular reasoning agent system carrying out the benchmark task. Following a ReAct-style approach the models are instructed to break the task into smaller steps, reasoning through them, and calling specific tools as needed.
+
+The software architecture will:
+
+* Expose a clear set of tools (e.g., RAG, web-search, short-term memory, access to academic and clinical resources, logging and scoring utilities).  
+* Allow swapping the underlying Models (local vs. self-hosted vs. cloud) with minimal changes.  
+* Support different data backends (e.g., local files, on-premise storage, or de-identified payloads for cloud calls).
+
+The system will be highly modular, so each deployment tier reuses as much of the common pipeline as possible, while allowing for minor inference optimizations such as prompt engineering and hyper parameter tuning. This should reduce variance and noise when evaluating performance and makes later extensions easier.
+
+### **Comparative experiment and evaluation setup**
+
+The main experiment will compare three deployment tiers of the same agent pipeline:
+
+1. Tier 1 – Local / on‑device model  
+2. Tier 2 – Self‑hosted / on‑premise model  
+3. Tier 3 – Cloud‑based model
+
+All three will be tested on the same software-infrastructure and graded by an evaluation harness on functional correctness (accuracy), performance (latency and throughput), privacy and compliance, and cost.
+
+The aim is not just to see which tier performs best, but to measure how much cognitive and analytical performance is lost when moving from Tier 3 down to Tier 1, and to judge whether that loss is acceptable for a clinical support tool.
+
+### **Open Questions**
+
+* Which clinical benchmark framework offers the best balance between feasibility (data access, licensing) and analytical feasibility?  
+* Will there be a need to synthesize interview data and if so, how can I ensure its academic integrity?  
+* What adaptations and aides will be necessary for less performant models to perform to an acceptable degree?  
+  * Will an agent pipeline designed around small models negatively impact the performance of larger models?
+
+The answers will influence both the experimental setup and the interpretation of results.
+
+## **Preliminary Structure**
+
+The thesis will be structured as follows:
+
+1. **Introduction**  
+2. **Methodology**  
+   1. Benchmark Framework: Selection and Formal   
+   2. **Data Sourcing and Preparation**  
+   3. **System Architecture and Agent Framework Design**  
+   4. Grading and Evaluation   
+3. **Implementation**  
+4. **Evaluation and Results**  
+5. **Discussion**  
+6. **Conclusion and Future Work**
+
+## **Roadmap**
+
+* **Weeks 1–2 – Define the project**  
+  * Finalize the research question, task (PHQ‑8 on DAIC‑WOZ), and evaluation metrics.  
+  * Decide on data preprocessing and any synthetic data generation.  
+  * Choose model lineup, tiers (cloud / self‑hosted / local), and hardware.  
+  * Draft the Methodology sections for data and overall setup.  
+* **Weeks 3–5 – Build and test the system**  
+  * Implement the agent framework and tool interfaces.  
+  * Integrate Tier 3 (cloud) and Tier 2 (self‑hosted) models and run pilot tests.  
+  * Set up Tier 1 (local) models and tune prompts/flows for smaller models.  
+  * Run end‑to‑end pilots and document the system architecture.  
+* **Weeks 6–7 – Run experiments and analyze**  
+  * Fix all experiment settings (models, tiers, hyperparameters, etc.).  
+  * Run full experiments on all tiers; collect accuracy, latency, and cost data.  
+  * Perform quantitative and light qualitative error analysis.  
+  * Draft the Evaluation & Results chapter with tables and figures.  
+* **Week 8 – Write core thesis sections**  
+  * Write the Discussion (interpretation, trade‑offs, limitations).  
+  * Write Introduction and Related Work  
+  * Update Methodology to match what was actually implemented.  
+* **Weeks 9–10 – Finalize and submit**  
+  * Produce a full integrated thesis draft and refine figures, tables, and references.  
+  * Incorporate feedback and ensure consistent terminology.  
+  * Add a technical appendix (models, hardware, hyperparameters, code overview).  
+  * Prepare and submit the final thesis and any required materials.
+
+## **References**
+
+- *Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022, October 6). REACT: Synergizing reasoning and acting in language models. arXiv.org. [https://arxiv.org/abs/2210.03629](https://arxiv.org/abs/2210.03629)*  
+- *Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires*. (n.d.). [https://arxiv.org/html/2510.25384v1](https://arxiv.org/html/2510.25384v1)  
+- *How real are synthetic therapy conversations? Evaluating fidelity in prolonged exposure dialogues*. (n.d.). [https://arxiv.org/html/2504.21800v1](https://arxiv.org/html/2504.21800v1)  
+- Nisevic, M., Milojevic, D., & Spajic, D. (2025). Synthetic data in medicine: Legal and ethical considerations for patient profiling. *Computational and Structural Biotechnology Journal*, *28*, 190–198. [https://doi.org/10.1016/j.csbj.2025.05.026](https://doi.org/10.1016/j.csbj.2025.05.026)  
+- *Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of affective disorders, 114(1-3), 163–173. [https://doi.org/10.1016/j.jad.2008.06.026](https://doi.org/10.1016/j.jad.2008.06.026)*  
+- *National Comorbidity Survey (NCS) series*. (n.d.). [https://www.icpsr.umich.edu/web/ICPSR/series/00527](https://www.icpsr.umich.edu/web/ICPSR/series/00527)  
+- *National Comorbidity Survey*. (n.d.). [https://web.archive.org/web/20250614210732/https://hcp.med.harvard.edu/ncs/replication.php](https://web.archive.org/web/20250614210732/https://hcp.med.harvard.edu/ncs/replication.php)  
+- Chand, S. P., Kuckel, D. P., & Huecker, M. R. (2023). Cognitive behavior therapy. In *StatPearls*. StatPearls Publishing. [https://www.ncbi.nlm.nih.gov/books/NBK470241/](https://www.ncbi.nlm.nih.gov/books/NBK470241/)  
+- *Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of affective disorders, 114(1-3), 163–173. [https://doi.org/10.1016/j.jad.2008.06.026](https://doi.org/10.1016/j.jad.2008.06.026)*  
+- *Zhang, M., Yang, X., Zhang, X., Labrum, T., Chiu, J. C., Eack, S. M., Fang, F., Wang, W. Y., & Chen, Z. Z. (2024, October 17). CBT-Bench: Evaluating large language models on assisting cognitive behavior therapy. arXiv.org. [https://arxiv.org/abs/2410.13218](https://arxiv.org/abs/2410.13218)*  
+- Li, Y., Yao, J., Bunyi, J. B. S., Frank, A. C., Hwang, A., & Liu, R. (2025, June 10). CounselBench: A Large-Scale expert evaluation and adversarial benchmarking of large language models in mental health question answering. arXiv.org. https://arxiv.org/abs/2506.08584

example.config.yaml

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+# Helia Configuration Example
+
+command: assess
+
+input_file: "path/to/transcript.txt"
+model: "gpt-4o"
+prompt_id: "default"
+temperature: 0.0
+
+database:
+  uri: "mongodb://localhost:27017"
+  database_name: "helia"

plans/agentic-architecture-phq8.md

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+# Plan: Modular Agentic Framework for Clinical Assessment (Helia)
+
+## Overview
+
+Implement a production-grade, privacy-first Agentic Framework using LangGraph to automate PHQ-8 clinical assessments. The system allows dynamic switching between Local (Tier 1), Self-Hosted (Tier 2), and Cloud (Tier 3) models to benchmark performance.
+
+## Problem Statement
+
+The current system relies on a monolithic script (`src/helia/agent/workflow.py` is a placeholder) and a single-pass evaluation logic that likely underperforms on smaller local models. To prove the thesis hypothesis—that local models can match cloud performance—we need a sophisticated **Stateful Architecture** that implements Multi-Stage Reasoning ("RISEN" pattern) and robust Human-in-the-Loop (HITL) workflows.
+
+## Proposed Solution
+
+A **Hierarchical Agent Supervisor** architecture built with **LangGraph**:
+
+1.  **Supervisor**: Orchestrates the workflow and manages state.
+2.  **Assessment Agent**: Implements the "RISEN" (Reasoning Improvement via Stage-wise Evaluation Network) pattern:
+    *   **Extract**: Quote relevant patient text.
+    *   **Map**: Align quotes to PHQ-8 criteria.
+    *   **Score**: Assign 0-3 value.
+3.  **Ingestion**: Standardizes data from S3/Local into a `ClinicalState`.
+4.  **Benchmarking**: Automates the comparison between Generated Scores vs. Ground Truth (DAIC-WOZ labels).
+
+**Note:** A dedicated **Safety Guardrail** agent has been designed but is scoped out of this MVP. See `plans/safety-guardrail-architecture.md` for details.
+
+## Technical Approach
+
+### Architecture: The "Helia Graph"
+
+```mermaid
+graph TD
+    Start --> Ingestion
+    Ingestion --> Router{Router}
+
+    subgraph "Assessment Agent (RISEN)"
+        Router --> Extract[Extract Evidence]
+        Extract --> Map[Map to Criteria]
+        Map --> Score[Score Item]
+        Score --> NextItem{Next Item?}
+        NextItem -- Yes --> Extract
+    end
+
+    NextItem -- No --> HumanReview["Human Review (HITL)"]
+    HumanReview --> Finalize[Finalize & Persist]
+```
+
+### Implementation Phases
+
+#### Phase 1: Core Graph & State Management (Foundation)
+*   **Goal**: Establish the LangGraph structure and Pydantic State.
+*   **Deliverables**:
+    *   `src/helia/agent/state.py`: Define `ClinicalState` (transcript, current_item, scores).
+    *   `src/helia/agent/graph.py`: Define the main `StateGraph` with Ingestion -> Assessment -> Persistence nodes.
+    *   `src/helia/ingestion/loader.py`: Add "Ground Truth" loading for DAIC-WOZ labels (critical for benchmarking).
+
+#### Phase 2: The "RISEN" Assessment Logic
+*   **Goal**: Replace monolithic `PHQ8Evaluator` with granular nodes.
+*   **Deliverables**:
+    *   `src/helia/agent/nodes/assessment.py`: Implement `extract_node`, `map_node`, `score_node`.
+    *   `src/helia/prompts/`: Create specialized prompt templates for each stage (optimized for Llama 3).
+    *   **Refactor**: Update `PHQ8Evaluator` to be callable as a tool/node rather than a standalone class.
+
+#### Phase 3: Tier Switching & Execution
+*   **Goal**: Implement dynamic model config.
+*   **Deliverables**:
+    *   `src/helia/configuration.py`: Ensure `RunConfig` (Tier 1/2/3) propagates to LangGraph `configurable` params.
+    *   `src/helia/agent/runner.py`: CLI entry point to run batch benchmarks.
+
+#### Phase 4: Human-in-the-Loop & Persistence
+*   **Goal**: Enable clinician review and data saving.
+*   **Deliverables**:
+    *   **Checkpointing**: Configure MongoDB/Postgres checkpointer for LangGraph.
+    *   **Review Flow**: Implement the `interrupt_before` logic for the "Finalize" node.
+    *   **Metrics**: Calculate "Item-Level Agreement" (MAE/Kappa) between Agent and Ground Truth.
+
+## Acceptance Criteria
+
+### Functional Requirements
+- [ ] **Stateful Workflow**: System successfully transitions Ingest -> Assess -> Persist using LangGraph.
+- [ ] **Multi-Stage Scoring**: Each PHQ-8 item is scored using the Extract -> Map -> Score pattern.
+- [ ] **Model Swapping**: Can run the *exact same graph* with `gpt-4` (Tier 3) and `llama3` (Tier 1) just by changing config.
+- [ ] **Benchmarking**: Automatically output a CSV comparing `Model_Score` vs `Human_Label` for all 8 items.
+
+### Non-Functional Requirements
+- [ ] **Privacy**: Tier 1 execution sends ZERO bytes to external APIs.
+- [ ] **Reproducibility**: Every run logs the exact prompts used and model version to MongoDB.
+
+## Dependencies & Risks
+- **Risk**: Local models (Tier 1) may hallucinate formatting in the "Map" stage.
+    *   *Mitigation*: Use `instructor` or constrained decoding (JSON mode) for Tier 1.
+- **Dependency**: Requires DAIC-WOZ dataset (assumed available locally or mocked).
+
+## References
+- **LangGraph**: [State Management](https://langchain-ai.github.io/langgraph/concepts/high_level/#state)
+- **Clinical Best Practice**: [RISEN Framework (2025)](https://pubmed.ncbi.nlm.nih.gov/40720397/)
+- **Project Config**: `src/helia/configuration.py`

plans/safety-guardrail-architecture.md

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+# Plan: Safety Guardrail Architecture (Post-MVP)
+
+## Overview
+
+A dedicated, parallel **Safety Guardrail Agent** designed to monitor clinical sessions for immediate risks (self-harm, suicidal ideation) and intervene regardless of the primary assessment agent's state. This component is critical for "Duty of Care" compliance but is scoped out of the initial MVP to focus on the core scoring pipeline.
+
+## Problem Statement
+
+General-purpose reasoning agents (like the PHQ-8 scorer) often exhibit "tunnel vision," focusing exclusively on their analytical task while missing or delaying the flagging of critical safety signals. In a clinical context, waiting for a 60-second reasoning loop to finish before flagging a suicide risk is unacceptable.
+
+## Proposed Solution
+
+A **Parallel Supervisor** pattern where the Safety Agent runs asynchronously alongside the main Assessment Agent.
+
+### Architecture
+
+```mermaid
+graph TD
+    Router{Router}
+
+    subgraph "Main Flow"
+        Router --> Assessment[Assessment Agent]
+    end
+
+    subgraph "Safety Layer"
+        Router --> Safety[Safety Guardrail]
+        Safety --> |Risk Detected| Interrupt[Interrupt Signal]
+    end
+
+    Assessment --> Merger
+    Interrupt --> Merger
+    Merger --> Handler{Risk Handling}
+```
+
+## Technical Approach
+
+### 1. The Safety Agent Node
+*   **Model**: Uses a smaller, faster model (e.g., Llama-3-8B-Instruct or a specialized BERT classifier) optimized for classification, not reasoning.
+*   **Prompting**: Few-shot prompted specifically for:
+    *   Suicidal Ideation (Passive vs Active)
+    *   Self-Harm Intent
+    *   Harm to Others
+*   **Output**: Boolean flag (`risk_detected`) + `risk_category` + `evidence_snippet`.
+
+### 2. Parallel Execution in LangGraph
+*   **Fan-Out**: The Supervisor node spawns *both* `assessment_node` and `safety_node` for every transcript chunk.
+*   **Race Condition Handling**:
+    *   If `safety_node` returns `risk_detected=True`, it must trigger a **`NodeInterrupt`** or inject a high-priority state update that overrides the Assessment Agent's output.
+
+### 3. Integration Points (Post-MVP)
+*   **State Schema**:
+    ```python
+    class ClinicalState(BaseModel):
+        # ... existing fields ...
+        safety_flags: List[SafetyAlert] = []
+        is_session_halted: bool = False
+    ```
+*   **Transition Logic**:
+    If `is_session_halted` becomes True, the graph routes immediately to a "Crisis Protocol" node, bypassing all remaining PHQ-8 items.
+
+## Implementation Plan
+
+1.  **Define Safety Schema**: Create `SafetyAlert` Pydantic model.
+2.  **Implement Guardrail Node**: Create `src/helia/agent/nodes/safety.py`.
+3.  **Update Graph**: Modify `src/helia/agent/graph.py` to add the parallel edge.
+4.  **Test Scenarios**: Create synthetic transcripts with hidden self-harm indicators to verify interruption works.
+
+## References
+*   [EmoAgent: Assessing and Safeguarding Human-AI Interaction (2025)](https://www.semanticscholar.org/paper/110ab0beb74ffb7ab1efe55ad36b4732835fa5c9)

todos/001-completed-p1-security-exception-handling.md

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+---
+status: pending
+priority: p1
+issue_id: "001"
+tags: ["security", "refactor", "python"]
+dependencies: []
+---
+
+# Fix S110 Security Issue in Extractor
+
+Replace `try-except-pass` block in `src/helia/analysis/extractor.py` with specific exception handling and logging.
+
+## Problem Statement
+
+The Security Sentinel identified a distinct security risk (S110) in `src/helia/analysis/extractor.py`. A `try-except-pass` block silently suppresses errors, making debugging impossible and potentially hiding security-critical failures or data corruption issues.
+
+## Findings
+
+- **File:** `src/helia/analysis/extractor.py`
+- **Issue:** S110 - `try-except-pass` detected.
+- **Impact:** Critical for visibility and system stability. Silent failures can lead to unpredictable application states.
+
+## Proposed Solutions
+
+### Option 1: Log and Re-raise
+
+**Approach:** Catch the specific exception, log the error with a traceback, and optionally re-raise it if the application cannot recover.
+
+**Pros:**
+- Full visibility into errors.
+- Prevents silent failures.
+
+**Cons:**
+- May require error handling changes upstream if exceptions are raised.
+
+### Option 2: Log and Continue (Safe Fallback)
+
+**Approach:** Catch specific exception, log it as an error/warning, and set a safe default value or continue processing if appropriate.
+
+**Pros:**
+- Prevents application crash while maintaining visibility.
+
+**Cons:**
+- Might mask severity if logs aren't monitored.
+
+## Recommended Action
+
+**To be filled during triage.**
+
+## Technical Details
+
+**Affected files:**
+- `src/helia/analysis/extractor.py`
+
+## Resources
+
+- **Source:** Security Sentinel Report
+
+## Acceptance Criteria
+
+- [ ] `try-except-pass` block removed.
+- [ ] Specific exception type caught (not bare `except:`).
+- [ ] Error logged using `logging` module (not `print`).
+- [ ] Unit tests added to verify exception handling behavior.
+
+## Work Log
+
+### 2025-12-20 - Initial Creation
+
+**By:** Claude Code
+
+**Actions:**
+- Created todo based on Security Sentinel findings.

todos/002-completed-p2-magic-numbers-refactor.md

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+---
+status: pending
+priority: p2
+issue_id: "002"
+tags: ["refactor", "maintainability", "python"]
+dependencies: []
+---
+
+# Refactor PHQ-8 Scoring Magic Numbers
+
+Extract PHQ-8 scoring constants in `src/helia/assessment/core.py` to improve maintainability and readability.
+
+## Problem Statement
+
+The Kieran Python Reviewer and Pattern Recognition Specialist identified "magic numbers" in the PHQ-8 scoring logic within `src/helia/assessment/core.py`. Hardcoded values make the code difficult to understand and risky to modify.
+
+## Findings
+
+- **File:** `src/helia/assessment/core.py`
+- **Issue:** Hardcoded integers representing PHQ-8 scoring thresholds or values.
+- **Recommendation:** Extract these into named constants.
+
+## Proposed Solutions
+
+### Option 1: Class-level Constants
+
+**Approach:** Define capitalized constants (e.g., `MIN_SCORE`, `SEVERE_THRESHOLD`) at the top of the class or module.
+
+**Pros:**
+- Improves readability (intent is clear).
+- Single source of truth for changes.
+
+**Cons:**
+- None significant.
+
+## Recommended Action
+
+**To be filled during triage.**
+
+## Technical Details
+
+**Affected files:**
+- `src/helia/assessment/core.py`
+
+## Resources
+
+- **Source:** Kieran Python Reviewer / Pattern Recognition Specialist
+
+## Acceptance Criteria
+
+- [ ] All magic numbers in PHQ-8 logic replaced with named constants.
+- [ ] Constants defined at module or class level.
+- [ ] Logic remains functionally identical (verify with tests if available).
+
+## Work Log
+
+### 2025-12-20 - Initial Creation
+
+**By:** Claude Code
+
+**Actions:**
+- Created todo based on code review findings.

todos/003-completed-p2-logging-migration.md

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+---
+status: pending
+priority: p2
+issue_id: "003"
+tags: ["ops", "quality", "python"]
+dependencies: []
+---
+
+# Switch to Logging in Main
+
+Replace `print` statements with the standard `logging` module in `src/helia/main.py`.
+
+## Problem Statement
+
+`src/helia/main.py` uses `print` statements for output. This prevents proper log level management, timestamping, and integration with monitoring systems.
+
+## Findings
+
+- **File:** `src/helia/main.py`
+- **Issue:** Use of `print` for logging information.
+- **Impact:** Ops/Visibility reduced.
+
+## Proposed Solutions
+
+### Option 1: Standard Logging
+
+**Approach:** Import `logging`, configure a basic logger, and replace `print()` calls with `logger.info()`, `logger.error()`, etc.
+
+**Pros:**
+- Standard practice.
+- Configurable output levels and formats.
+
+**Cons:**
+- Slight initial setup overhead.
+
+## Recommended Action
+
+**To be filled during triage.**
+
+## Technical Details
+
+**Affected files:**
+- `src/helia/main.py`
+
+## Resources
+
+- **Source:** Kieran Python Reviewer
+
+## Acceptance Criteria
+
+- [ ] `logging` module imported and configured.
+- [ ] All diagnostic `print` statements replaced with `logger` calls.
+- [ ] Import organization fixed in `src/helia/main.py` (mentioned in findings).
+
+## Work Log
+
+### 2025-12-20 - Initial Creation
+
+**By:** Claude Code
+
+**Actions:**
+- Created todo based on code review findings.

todos/004-completed-p2-namespace-packages.md

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+---
+status: pending
+priority: p2
+issue_id: "004"
+tags: ["security", "reliability", "python"]
+dependencies: []
+---
+
+# Add __init__.py to Namespace Packages
+
+Add `__init__.py` files to implicit namespace packages to prevent import hijacking and ensure correct package resolution.
+
+## Problem Statement
+
+Both Security Sentinel (INP001) and Kieran Python Reviewer identified missing `__init__.py` files. While Python 3 supports implicit namespace packages, omitting `__init__.py` in standard packages can lead to ambiguity and potential security risks (import hijacking).
+
+## Findings
+
+- **Issue:** Missing `__init__.py` files.
+- **Impact:** Security/Reliability. INP001 warning.
+
+## Proposed Solutions
+
+### Option 1: Add Empty __init__.py
+
+**Approach:** Create empty `__init__.py` files in all directory levels that function as packages.
+
+**Pros:**
+- Explicitly defines packages.
+- Resolves INP001.
+
+**Cons:**
+- Adds file clutter (minor).
+
+## Recommended Action
+
+**To be filled during triage.**
+
+## Technical Details
+
+**Likely locations:**
+- `src/helia` (check if present)
+- Subdirectories in `src/helia` where they are missing.
+
+## Resources
+
+- **Source:** Security Sentinel / Kieran Python Reviewer
+
+## Acceptance Criteria
+
+- [ ] `__init__.py` files added to all relevant source directories.
+- [ ] Package imports verify correctly.
+
+## Work Log
+
+### 2025-12-20 - Initial Creation
+
+**By:** Claude Code
+
+**Actions:**
+- Created todo based on code review findings.

todos/005-completed-p3-code-cleanup.md

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+---
+status: pending
+priority: p3
+issue_id: "005"
+tags: ["cleanup", "quality", "python"]
+dependencies: []
+---
+
+# General Code Quality Cleanup
+
+Address various code quality issues including unused arguments, type ignores, and list optimizations.
+
+## Problem Statement
+
+Multiple reviewers identified smaller code quality issues that accumulate to technical debt. These include unused arguments in `workflow.py`, specific type ignores in `db.py`, and list comprehension optimizations in `core.py`.
+
+## Findings
+
+1. **`src/helia/assessment/core.py`**:
+   - Optimize list comprehension.
+2. **`src/helia/agent/workflow.py`**:
+   - Rename unused `state` arguments to `_state`.
+3. **`src/helia/assessment/core.py`**:
+   - Use dependency injection for `PHQ8Evaluator`.
+4. **`src/helia/db.py`**:
+   - PGH003: Narrow `type: ignore` to `type: ignore[arg-type]`.
+5. **General**:
+   - Recommends generator expression for join operations.
+
+## Proposed Solutions
+
+### Option 1: Batch Cleanup
+
+**Approach:** Go through each file and apply the specific fix.
+
+**Pros:**
+- Cleans up "broken windows".
+- Improves linting scores.
+
+## Recommended Action
+
+**To be filled during triage.**
+
+## Technical Details
+
+**Affected files:**
+- `src/helia/assessment/core.py`
+- `src/helia/agent/workflow.py`
+- `src/helia/db.py`
+
+## Resources
+
+- **Source:** Kieran Python Reviewer, Security Sentinel, Pattern Recognition Specialist
+
+## Acceptance Criteria
+
+- [ ] `src/helia/assessment/core.py`: List comprehension optimized.
+- [ ] `src/helia/agent/workflow.py`: Unused args renamed to `_state`.
+- [ ] `src/helia/db.py`: `type: ignore` narrowed.
+- [ ] `src/helia/assessment/core.py`: Dependency injection pattern reviewed/applied.
+
+## Work Log
+
+### 2025-12-20 - Initial Creation
+
+**By:** Claude Code
+
+**Actions:**
+- Created todo based on aggregated code review findings.