Assessing the methodologic quality of systematic reviews using generative large language models
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate whether generative large language models (LLMs) can accurately assess the methodologic quality of systematic reviews (SRs). METHODS: A total of 114 SRs from five leading urology journals were included in the study. Human reviewers graded each of the SRs in duplicate, with differences adjudicated by a third expert. We created a customized generative artificial intelligence (generative pre-trained transformer [GPT]), "Urology AMSTAR 2 Quality Assessor," and graded the 114 SRs in three iterations using a zero-shot method. We performed an enhanced trial focusing on critical criteria by giving GPT detailed, step-by-step instructions for each of the SRs using chain-of-thought method. Accuracy, sensitivity, specificity, and F1 score for each GPT trial were calculated against human results. Internal validity among three trials were computed. RESULTS: GPT had an overall congruence of 75%, with 77% in critical criteria and 73% in non-critical criteria when compared to human results. The average F1 score was 0.66. There was a high internal validity at 85% among three iterations. GPT accurately assigned 89% of studies into the correct overall category. When given specific, step-by-step instructions, congruence of critical criteria improved to 91%, and overall quality assessment accuracy to 93%. CONCLUSIONS: GPT showed promising ability to efficiently and accurately assess the quality of SRs in urology.
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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 | 0.659 | 0.812 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".