Development and validation of study filters for identifying controlled non‐randomized studies in <scp>PubMed</scp> and Ovid <scp>MEDLINE</scp>
Bibliographic record
Abstract
A retrospective analysis published by the German Institute for Quality and Efficiency in Health Care (IQWiG) in 2018 concluded that no filter for non-randomized studies (NRS) achieved sufficient sensitivity (≥92%), a precondition for comprehensive information retrieval. New NRS filters are therefore required, taking into account the challenges related to this study type. Our evaluation focused on the development of study filters for NRS with a control group ("controlled NRS"), as this study type allows the calculation of an effect size. In addition, we assumed that due to the more explicit search syntax, controlled NRS are easier to identify than non-controlled ones, potentially resulting in better performance measures of study filters for controlled NRS. Our aim was to develop study filters for identifying controlled NRS in PubMed and Ovid MEDLINE. We developed two new search filters that can assist clinicians and researchers in identifying controlled NRS in PubMed and Ovid MEDLINE. The reference set was based on 2110 publications in Medline extracted from 271 Cochrane reviews and on 4333 irrelevant references. The first filter maximizes sensitivity (92.42%; specificity 79.67%, precision 68.49%) and should be used when a comprehensive search is needed. The second filter maximizes specificity (92.06%; precision 82.98%, sensitivity 80.94%) and should be used when a more focused search is sufficient.
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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 | 0.518 | 0.745 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.032 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".