# CODE AUDIT REPORT: Submission 269
## TEAM-PHI: Multi-Agent PHI De-identification Framework

**Date:** 2024
**Auditor:** Autonomous Code Auditing System

---

## EXECUTIVE SUMMARY

This submission presents a multi-agent framework (TEAM-PHI) for evaluating PHI de-identification models. The code is **INCOMPLETE and NOT FULLY FUNCTIONAL** as submitted. While the overall architecture is reasonable and the methodology appears sound, the code contains critical missing components that prevent independent execution.

**Overall Assessment:** HIGH severity issues prevent reproduction
**Agent Reproducibility:** FALSE (no evidence of AI-generated code prompts)

---

## DETAILED FINDINGS

### 1. COMPLETENESS & STRUCTURAL INTEGRITY

#### CRITICAL Issues:

**1.1 Missing Data Files (CRITICAL)**
- **Location:** All scripts reference `"folder_contain_clinical_notes"` or similar
- **Issue:** No clinical notes data is provided in the submission
- **Evidence:**
  - GPT_Deid.py line 27: `input_dir = "folder_contain_clinical_notes"`
  - Llama_Deid.py line 10: `input_dir = "folder_contain_clinical_notes"`
  - All evaluation scripts reference this directory
- **Impact:** Cannot execute any part of the pipeline without data
- **Justification:** The Reproducibility Statement acknowledges this: "Due to the sensitive nature of medical data and privacy regulations, these clinical notes are not publicly available"
- **Severity:** CRITICAL for execution, but JUSTIFIED due to privacy constraints

**1.2 Hardcoded Placeholder Credentials (CRITICAL)**
- **Location:**
  - GPT_Deid.py lines 5, 13-14
  - GPT_Eval.py lines 7, 12, 14
  - GPT_vote.py lines 5, 13-14
- **Evidence:**
  ```python
  endpoint = "your_Azure_endpoint"
  subscription_key = "your_Azure_subscription_key"
  api_version = "your_api_version"
  ```
  - GPT_vote.py has empty strings: `endpoint = ""`
- **Impact:** GPT-based scripts will fail immediately on execution
- **Severity:** CRITICAL - Code cannot run as written

**1.3 Missing Intermediate Results (HIGH)**
- **Location:** Evaluation agents expect processed De-id outputs
- **Evidence:**
  - GPT_Eval.py line 23: `"De-id Agents' Final Output/gemma2_deid_outputs.json"`
  - No such directory or files exist in submission
- **Impact:** Cannot run evaluation stage independently
- **Severity:** HIGH - Breaks pipeline modularity

**1.4 Missing Model Directories (HIGH)**
- **Location:** All local model scripts
- **Evidence:**
  - Llama_Deid.py line 6: `local_model_dir = "llama-3-8B-Instruct"`
  - LPPA_Deid.py line 6: `local_model_dir = "108mix4ktest1"`
  - Gemma_Deid.py line 9: `MODEL_DIR = "gemma-2-9b-it-mlx-q4"`
- **Impact:** Scripts expect pre-downloaded models in specific directories
- **Severity:** HIGH - Expected for large models but not documented

**1.5 Hardcoded Results in Voting Scripts (HIGH)**
- **Location:** All voting scripts
- **Evidence:**
  - GPT_vote.py lines 30-32: `llm_results = """\n\n"""`
  - Llama_vote.py lines 30-32: `llm_results = """\ndeid agents and evaluation agents' output\n"""`
  - Gemma_Mistral_vote.py lines 16-18: `llm_results = r"""\n\n"""`
- **Impact:** Voting scripts require manual insertion of results
- **Severity:** HIGH - Not automated as described in paper

**1.6 Missing Requirements File (MEDIUM)**
- **Location:** Root directory
- **Evidence:** No requirements.txt file found, despite README mentioning it
- **README Quote:** "**['requirements.txt' file could be used to download the python packages automatically]**"
- **Impact:** Dependency versions not specified (except in README)
- **Severity:** MEDIUM - Versions are listed in README but not in standard format

#### Positive Observations:

✓ **No TODO comments or placeholder functions** - All functions are fully implemented
✓ **No hardcoded results** in de-identification or evaluation outputs
✓ **Proper error handling** in Gemma/Mistral scripts with try-except blocks
✓ **Complete implementation** of all core algorithms (JSON parsing, normalization, evaluation)
✓ **Main entry points exist** for all components

---

### 2. RESULTS AUTHENTICITY

#### NO RED FLAGS DETECTED:

✓ **No hardcoded experimental results** - All metrics computed programmatically
✓ **No cherry-picked seeds** - Temperature and sampling parameters are reasonable and consistent
✓ **No manual result insertion** - All outputs generated from model inference
✓ **Proper metric computation:**
  - Precision calculated as: `total_correct / total_pairs`
  - Coverage calculated as: `total_pairs / total_tokens`
  - Recall-proxy calculated using average across models
  - All metrics derived from actual evaluation, not hardcoded

**Assessment:** Code demonstrates authentic computational workflow. Results would be genuinely computed if data were available.

---

### 3. IMPLEMENTATION-PAPER CONSISTENCY

#### Strong Alignment:

✓ **Guideline prompts match paper description** (De-identification agents follow specified entity types)
✓ **Evaluation methodology matches paper** (JSON output format: `{"Number of Correct Pairs": N}`)
✓ **Multi-agent voting implemented as described** (independent and cross-informed modes)
✓ **Normalization rules comprehensive:**
  - Title removal for PERSON entities (lines 46-57 in Eval scripts)
  - Date format standardization (DATE_PAT regex in Gemma/Mistral Eval)
  - Deduplication logic properly implemented
✓ **Metrics match paper definitions:**
  - Precision, Coverage, Recall-Proxy all implemented correctly
  - Entity-specific metrics for PERSON and DATE_TIME categories

#### Minor Inconsistencies:

⚠ **Temperature settings vary:**
  - De-id models use temperature 0.5-1.0
  - Evaluation agents mostly use 0.0 (deterministic)
  - Voting uses 0.0 (Gemma/Mistral) or 1.0 (GPT/Llama)
  - **Assessment:** MINOR - Reasonable design choices, though not fully documented

⚠ **Max tokens vary by stage:**
  - De-id: 256-4096 tokens
  - Evaluation: 512-1024 tokens
  - Voting: 8-4096 tokens
  - **Assessment:** MINOR - Appropriate for different tasks

---

### 4. CODE QUALITY SIGNALS

#### Positive Indicators:

✓ **Sophisticated JSON parsing** with multiple fallback strategies (Gemma/Mistral Deid)
✓ **Extensive normalization logic** showing deep understanding of PHI matching challenges
✓ **Consistent code structure** across similar agents (GPT/Llama variants follow same patterns)
✓ **Proper use of transformers library** with chat templates and generation parameters
✓ **No excessive code duplication** - Similar functionality appropriately parameterized
✓ **Clear separation of concerns** - De-id, Evaluation, and Voting are distinct modules

#### Quality Concerns:

⚠ **Platform-specific implementations:**
  - Gemma/Mistral use `mlx_lm` (Apple Silicon specific)
  - Llama uses standard `transformers`
  - **Assessment:** MINOR - Appropriate for stated hardware (README mentions MacBook + H100)

⚠ **Limited error handling in some scripts:**
  - GPT/Llama de-id scripts have no try-except blocks
  - Could fail silently on malformed JSON
  - **Assessment:** LOW - Production code would need more robust error handling

⚠ **Inconsistent commenting:**
  - Some scripts well-commented (Gemma/Mistral with detailed docstrings)
  - Others minimal (GPT/Llama basic scripts)
  - **Assessment:** LOW - Stylistic, doesn't affect functionality

---

### 5. FUNCTIONALITY INDICATORS

#### Evidence of Real Development:

✓ **Multiple implementation approaches** (MLX vs transformers) suggest iterative development
✓ **Sophisticated regex patterns** for date parsing and normalization indicate domain expertise
✓ **Detailed normalization rules** (title removal, punctuation stripping) show understanding of PHI matching challenges
✓ **Fallback parsing strategies** in Gemma/Mistral scripts demonstrate debugging experience
✓ **CSV output options** with argparse in advanced evaluation scripts
✓ **Sleep timers** to prevent API rate limiting (practical consideration)

#### Pipeline Stages:

1. **De-identification:** ✓ Complete implementation
2. **Evaluation:** ✓ Complete implementation (requires De-id outputs)
3. **Voting:** ✓ Complete but requires manual result insertion
4. **End-to-end automation:** ✗ Missing - No master script to run full pipeline

---

### 6. DEPENDENCY & ENVIRONMENT ISSUES

#### Dependencies (from README):

✓ Standard packages with reasonable versions:
- python==3.8.10
- openai==0.28.1 (older version, pre-1.0 API)
- transformers==4.33.3
- torch==1.10.0+cu111

⚠ **Potential Issues:**
- MLX framework (Apple-specific) not in README requirements
- `mlx_lm` package used but not listed
- Old OpenAI API version (0.28.1) - newer code uses new import style

**Assessment:** MEDIUM - Code uses newer OpenAI import (`from openai import AzureOpenAI`) but README lists old version

#### Computational Resources:

✓ **Realistic resource requirements:** Dual H100 GPUs mentioned for 70B models
✓ **Mixed hardware approach:** MacBook for smaller models, server for large ones
✓ **HIPAA-compliant infrastructure:** Azure for GPT models (appropriate for medical data)

---

### 7. ARCHITECTURAL ASSESSMENT

#### Framework Design:

✓ **Well-structured three-stage pipeline:**
  1. Multiple De-id models run in parallel
  2. Multiple Evaluation agents assess each model
  3. LLM voting aggregates judgments

✓ **Modular design:** Each component can theoretically run independently
✓ **Standardized I/O:** JSON format used consistently throughout
✓ **Sensible normalization:** Title removal, date standardization, deduplication

#### Missing Components:

✗ **No orchestration script** to run full pipeline
✗ **No data preprocessing scripts** (clinical notes to JSON)
✗ **No results aggregation scripts** (collect evaluations into voting input)
✗ **No ground-truth comparison scripts** (despite paper claiming F1=0.62±0.01 for Llama-70B)

---

## SPECIFIC RED FLAGS BY SEVERITY

### CRITICAL (Prevents Execution):
1. **Placeholder credentials** in GPT scripts - Code will fail immediately
2. **Missing data directory** - No input to process (justified by privacy)
3. **Missing intermediate outputs** - Evaluation scripts expect non-existent files

### HIGH (Major Gaps):
4. **Manual result insertion required** for voting stage - Not automated as implied
5. **Missing ground-truth evaluation code** - Paper reports F1 scores but no code to compute them
6. **Missing model download instructions** - Expected directories not documented
7. **Inconsistent dependency specifications** - MLX not in requirements, old OpenAI version listed

### MEDIUM (Quality Issues):
8. **No requirements.txt file** - Despite being mentioned in README
9. **No master orchestration script** - Each stage must be run manually
10. **Limited documentation** on expected file formats and directory structure

### LOW (Minor Issues):
11. **Inconsistent error handling** across scripts
12. **Variable commenting quality** across files
13. **Platform-specific code** (MLX) without clear documentation

---

## VERIFICATION AGAINST PAPER CLAIMS

### Claims We Can Verify:

✓ **Multi-agent architecture:** Code shows 8 De-id models (GPT-3.5, GPT-4o, Llama-8B, Llama-70B, Mistral-7B, Gemma-2, LPPA4k, LPPA5k)
✓ **6 Evaluation agents:** Code shows GPT-3.5, GPT-4o-mini, Llama-70B, Llama-8B(?), Gemma-2, Mistral-7B
✓ **JSON output format:** Consistently enforced across all stages
✓ **Normalization strategies:** Title removal, date parsing implemented
✓ **Two voting modes:** Independent and cross-informed guidelines present
✓ **Metrics computation:** Precision, Coverage, Recall-Proxy calculated correctly

### Claims We Cannot Verify:

✗ **F1 scores (Table 8):** No ground-truth comparison code provided
✗ **Human evaluation (Table 9):** No evaluation interface or results
✗ **100 clinical notes dataset:** Not provided (justified)
✗ **Total compute cost (200 GPU-hours):** No logging or timing code
✗ **Specific performance numbers:** Cannot reproduce without data and complete pipeline

---

## AGENT REPRODUCIBILITY ASSESSMENT

**Question:** Does the submission document AI usage with prompts that could regenerate the code?

**Answer:** FALSE

**Reasoning:**
1. ✗ No ChatGPT conversation logs found
2. ✗ No AI generation prompts documented
3. ✗ No comments indicating AI-assisted development
4. ✗ The Reproducibility Statement mentions "AI-generated results" referring to the *outputs of their LLM-based system*, not the code itself
5. ✓ Code quality and sophistication suggest human development with domain expertise
6. ✓ Multiple implementation styles (basic vs advanced) suggest iterative human refinement

**Conclusion:** This code was written by humans, not generated by AI assistants. The submission does not provide prompts for regenerating the code.

---

## AUTHENTICITY ASSESSMENT

### Evidence Code is GENUINE (Not Fabricated):

1. **No hardcoded results** - All metrics computed from actual model outputs
2. **Realistic implementation complexity** - JSON parsing with multiple fallbacks shows real-world problem-solving
3. **Platform-specific optimizations** - MLX for Apple Silicon shows practical hardware considerations
4. **Incomplete but consistent** - Missing pieces are infrastructure (data, credentials) not algorithms
5. **Sophisticated domain knowledge** - PHI normalization rules show medical informatics expertise

### Evidence Results COULD Be Reproduced:

1. ✓ All metrics have proper computational logic
2. ✓ No cherry-picking mechanisms visible
3. ✓ Sampling parameters are reasonable
4. ✓ Evaluation methodology is sound
5. ✗ **BUT:** Cannot verify without data access

**Assessment:** The code appears to represent genuine research work. Results would be computed authentically if the pipeline could run.

---

## RECOMMENDATIONS FOR REPRODUCIBILITY

### To Make Code Executable:

1. **Provide sample data** (synthetic or public dataset) to demonstrate pipeline
2. **Create master orchestration script** that runs all stages
3. **Add placeholder credentials detection** with informative error messages
4. **Include ground-truth evaluation scripts** to match Table 8 results
5. **Create requirements.txt** with all dependencies including MLX
6. **Document model download process** and expected directory structure
7. **Add data preprocessing scripts** showing clinical note → JSON conversion
8. **Include results aggregation scripts** for voting stage
9. **Provide example outputs** at each pipeline stage

### To Match Paper Claims:

10. **Include timing/logging code** to verify compute cost claims
11. **Add validation scripts** comparing automatic rankings to gold-standard
12. **Include human evaluation interface** or documentation
13. **Document hardware requirements** more clearly in code comments

---

## OVERALL ASSESSMENT

### Strengths:
- ✓ Core algorithms are complete and well-implemented
- ✓ No evidence of fabricated results
- ✓ Methodology matches paper description
- ✓ Code shows genuine domain expertise
- ✓ Modular architecture is sound
- ✓ Results would be authentic if pipeline could run

### Critical Weaknesses:
- ✗ Cannot execute as submitted (missing data, credentials, models)
- ✗ Missing orchestration and integration code
- ✗ Ground-truth evaluation code absent
- ✗ Manual intervention required for voting stage
- ✗ Incomplete documentation

### Final Verdict:

**CODE QUALITY:** Good - well-implemented algorithms with domain expertise

**COMPLETENESS:** Incomplete - missing infrastructure and integration layers

**EXECUTABILITY:** Non-functional - cannot run without data, credentials, and models

**AUTHENTICITY:** High confidence - genuine research, not fabricated

**REPRODUCIBILITY:** Low - major barriers prevent independent verification

**AGENT REPRODUCIBLE:** FALSE - no AI generation prompts provided

---

## SEVERITY SUMMARY

| Category | Count | Examples |
|----------|-------|----------|
| CRITICAL | 3 | Missing data, placeholder credentials, missing intermediate files |
| HIGH | 4 | Manual voting, missing ground-truth code, model directories |
| MEDIUM | 3 | No requirements.txt, dependency inconsistencies, no orchestration |
| LOW | 3 | Error handling, documentation, platform-specific code |

**Total Issues:** 13 (3 Critical, 4 High, 3 Medium, 3 Low)

---

## CONCLUSION

This submission represents **genuine research with incomplete code infrastructure**. The core algorithms are well-implemented and show no signs of result fabrication. However, critical components required for independent execution are missing:

1. Data (justified by privacy constraints)
2. Credentials (expected to be user-provided)
3. Integration scripts (should have been included)
4. Ground-truth evaluation (claimed in paper but absent)

The code could produce authentic results if completed, but **cannot currently verify paper claims** without substantial additional work. The submission earns credit for algorithmic completeness but loses points for lack of end-to-end reproducibility.

**Recommendation:** Request authors provide (1) synthetic/public sample data, (2) complete orchestration scripts, and (3) ground-truth evaluation code to enable independent verification.

---

**Report Generated:** 2024
**Audit System Version:** 1.0