GPT for RCTs?: Using AI to determine adherence to reporting guidelines
Bibliographic record
Abstract
Abstract Background Adherence to established reporting guidelines can improve clinical trial reporting standards, but attempts to improve adherence have produced mixed results. This exploratory study aimed to determine how accurate a Large Language Model generative AI system (AI-LLM) was for determining reporting guideline compliance in a sample of sports medicine clinical trial reports. Design and Methods This study was an exploratory retrospective data analysis. The OpenAI GPT-4 and Meta LLama2 AI-LLMa were evaluated for their ability to determine reporting guideline adherence in a sample of 113 published sports medicine and exercise science clinical trial reports. For each paper, the GPT-4-Turbo and Llama 2 70B models were prompted to answer a series of nine reporting guideline questions about the text of the article. The GPT-4-Vision model was prompted to answer two additional reporting guideline questions about the participant flow diagram in a subset of articles. The dataset was randomly split (80/20) into a TRAIN and TEST dataset. Hyperparameter and fine-tuning were performed using the TRAIN dataset. The Llama2 model was fine-tuned using the data from the GPT-4-Turbo analysis of the TRAIN dataset. Primary outcome measure: Model performance (F1-score, classification accuracy) was assessed using the TEST dataset. Results Across all questions about the article text, the GPT-4-Turbo AI-LLM demonstrated acceptable performance (F1-score = 0.89, accuracy[95% CI] = 90%[85-94%]). Accuracy for all reporting guidelines was > 80%. The Llama2 model accuracy was initially poor (F1-score = 0.63, accuracy[95%CI] = 64%[57-71%]), and improved with fine-tuning (F1-score = 0.84, accuracy[95%CI] = 83%[77-88%]). The GPT-4-Vision model accurately identified all participant flow diagrams (accuracy[95% CI] = 100%[89-100%]) but was less accurate at identifying when details were missing from the flow diagram (accuracy[95% CI] = 57%[39-73%]). Conclusions Both the GPT-4 and fine-tuned Llama2 AI-LLMs showed promise as tools for assessing reporting guideline compliance. Next steps should include developing an efficent, open-source AI-LLM and exploring methods to improve model accuracy.
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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 | 0.722 | 0.909 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".