Using large language models to directly screen electronic databases as an alternative to traditional search strategies such as the Cochrane highly sensitive search for filtering randomized controlled trials in systematic reviews
Bibliographic record
Abstract
A critical step in systematic reviews involves the definition of a search strategy, with keywords and Boolean logic, to filter electronic databases. We hypothesize that it is possible to screen articles in electronic databases using large language models (LLMs) as an alternative to search equations. To investigate this matter, we compared two methods to identify randomized controlled trials (RCTs) in electronic databases: filtering databases using the Cochrane highly sensitive search and an assessment by an LLM.We retrieved studies indexed in PubMed with a publication date between September 1 and September 30, 2024 using the sole keyword "diabetes." We compared the performance of the Cochrane highly sensitive search and the assessment of all titles and abstracts extracted directly from the database by GPT-4o-mini to identify RCTs. Reference standard was the manual screening of retrieved articles by two independent reviewers.The search retrieved 6377 records, of which 210 (3.5%) were primary reports of RCTs. The Cochrane highly sensitive search filtered 2197 records and missed one RCT (sensitivity 99.5%, 95% CI 97.4% to100%; specificity 67.8%, 95% CI 66.6% to 68.9%). Assessment of all titles and abstracts from the electronic database by GPT filtered 1080 records and included all 210 primary reports of RCTs (sensitivity 100%, 95% CI 98.3% to100%; specificity 85.9%, 95% CI 85.0% to 86.8%).LLMs can screen all articles in electronic databases to identify RCTs as an alternative to the Cochrane highly sensitive search. This calls for the evaluation of LLMs as an alternative to rigid search strategies.
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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 | 0.483 | 0.750 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.053 | 0.041 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".