Consistency and accuracy of indexing systematic review articles and meta‐analyses in <scp>medline</scp>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it