Large Language Models for Automating Clinical Trial Criteria Conversion to Observational Medical Outcomes Partnership Common Data Model Queries: Validation and Evaluation Study
Bibliographic record
Abstract
Background: Real-world data-based feasibility assessments enhance clinical trial design, but automating eligibility criteria conversion to database queries is hindered by challenges related to ensuring high accuracy and generating clear, usable outputs. Objective: The aim of this study is to develop an automated system converting free-text eligibility criteria from ClinicalTrials.gov into Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)-compatible Structured Query Language (SQL) queries and systematically evaluate hallucination patterns across multiple large language models (LLMs) to identify the optimal deployment strategies. Methods: Our system employs a three-stage preprocessing pipeline (segmentation, filtering, and simplification) achieving 58.2% token reduction while preserving clinical semantics. We compared GPT-4 concept mapping performance against USAGI using 357 clinical terms from 30 trials. For comprehensive evaluation, we analyzed 760 SQL generation attempts (19 trials×8 LLMs×5 prompting strategies) using the SynPUF (Synthetic Public Use Files) dataset and validated selected queries against National COVID Cohort Collaborative reference concept sets using Asan Medical Center's OMOP CDM database. Results: GPT-4 achieved a 48.5% concept mapping accuracy versus USAGI's 32.0% (P<.001), with domain-specific performance ranging from 72.7% (drug) to 38.3% (measurement). Surprisingly, the open-source llama3: 8b model achieved the highest effective SQL rate (75.8%) compared to GPT-4 (45.3%), attributed to lower hallucination rates (21.1% vs 33.7%). The overall hallucination rate was 32.7%, with wrong domain assignments (34.2%) and placeholder insertions (28.7%) being the most common. Clinical validation revealed mixed performance: high concordance for type 1 diabetes (Jaccard=0.81), complete failure for pregnancy (Jaccard=0.00), and minimal overlap for type 2 diabetes (Jaccard=0.03), despite perfect overlap coefficients in both diabetes cases. Moderate performance was observed for uncontrolled hypertension (Jaccard=0.18). Conclusions: While LLMs can accelerate eligibility criteria transformation, hallucination rates of 21-50% necessitate careful model selection and validation strategies. Our findings challenge assumptions about model superiority, demonstrating that smaller, cost-effective models can outperform larger commercial alternatives. Future work should focus on hybrid approaches combining LLM capabilities with rule-based methods for handling complex clinical concepts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".