Preliminary comparison of the performance of the National Library of Medicine’s systematic review publication type and the sensitive clinical queries filter for systematic reviews in PubMed
Bibliographic record
Abstract
OBJECTIVE: The National Library of Medicine (NLM) inaugurated a "publication type" concept to facilitate searches for systematic reviews (SRs). On the other hand, clinical queries (CQs) are validated search strategies designed to retrieve scientifically sound, clinically relevant original and review articles from biomedical literature databases. We compared the retrieval performance of the SR publication type (SR[pt]) against the most sensitive CQ for systematic review articles (CQrs) in PubMed. METHODS: We ran date-limited searches of SR[pt] and CQrs to compare the relative yield of articles and SRs, focusing on the differences in retrieval of SRs by SR[pt] but not CQrs (SR[pt] NOT CQrs) and CQrs NOT SR[pt]. Random samples of articles retrieved in each of these comparisons were examined for SRs until a consistent pattern became evident. RESULTS: For SR[pt] NOT CQrs, the yield was relatively low in quantity but rich in quality, with 79% of the articles being SRs. For CQrs NOT SR[pt], the yield was high in quantity but low in quality, with only 8% being SRs. For CQrs AND SR[pt], the quality was highest, with 92% being SRs. CONCLUSIONS: We found that SR[pt] had high precision and specificity for SRs but low recall (sensitivity), whereas CQrs had much higher recall. SR[pt] OR CQrs added valid SRs to the CQrs yield at low cost (i.e., added few non-SRs). For searches that are intended to be exhaustive for SRs, SR[pt] can be added to existing sensitive search filters.
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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 | 0.536 | 0.851 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".