Consistency and accuracy of indexing systematic review articles and meta‐analyses in <scp>medline</scp>
Bibliographic record
Abstract
BACKGROUND: Systematic review articles support the advance of science and translation of research evidence into healthcare practice. Inaccurate retrieval from medline could limit access to reviews. OBJECTIVE: To determine the quality of indexing systematic reviews and meta-analyses in medline. METHODS: The Clinical Hedges Database, containing the results of a hand search of 161 journals, was used to test medline indexing terms for their ability to retrieve systematic reviews that met predefined methodologic criteria (labelled as 'pass' review articles) and reviews that reported a meta-analysis. RESULTS: The Clinical Hedges Database contained 49 028 articles; 753 were 'pass' review articles (552 with a meta-analysis). In total 758 review articles (independent of whether they passed) reported a meta-analysis. The search strategy that retrieved the highest number of 'pass' systematic reviews achieved a sensitivity of 97.1%. The publication type 'meta analysis' had a false positive rate of 5.6% (95% CI 3.9 to 7.6), and false negative rate of 0.31% (95% CI 0.26 to 0.36) for retrieving systematic reviews that reported a meta-analysis. CONCLUSIONS: Inaccuracies in indexing systematic reviews and meta-analyses in medline can be partly overcome by a 5-term search strategy. Introducing a publication type for systematic reviews of the literature could improve retrieval performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.662 | 0.928 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.065 | 0.051 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| 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".