Systematic review search strategies are poorly described and not reproducible: a cross-sectional meta-research study
Bibliographic record
Abstract
ABSTRACT Objective To determine the reproducibility of biomedical systematic review search strategies. Design Cross-sectional meta-research study. Population Random sample of 100 systematic reviews indexed in MEDLINE in November 2021. Main Outcome Measures The primary outcome measure is the percentage of systematic reviews for which all database searches can be reproduced. This was operationalized as fulfilling six key PRISMA-S reporting guideline items (database name, multi-database searching, full search strategies, limits and restrictions, date(s) of searches, and total records) and having all database searches reproduced within 10% of the number of original results. Results The 100 systematic review articles contained 453 database searches. Of those, 214 (47.2%) provided complete database information (named the database and platform; PRISMA-S item 1). Only 22 (4.9%) database searches reported all six PRISMA-S items. Forty-seven (10.4%) database searches could be reproduced within 10% of the number of results from the original search; 6 searches differed by more than 1000% between the originally reported number of results and the reproduction. Only one systematic review article provided the necessary details for all database searches to be fully reproducible. Conclusion Systematic review search reporting is poor. As systematic reviews and clinical practice guidelines based upon them continue to proliferate, so does research waste. To correct this will require a multi-faceted response from systematic review authors, peer reviewers, journal editors, and database providers.
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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 | 0.584 | 0.831 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".