Comparative analysis of Cochrane and non-Cochrane reviews over three decades
Bibliographic record
Abstract
BACKGROUND: Systematic reviews are viewed as the best study design to guide clinical decision-making as they are the least biased publications assuming they are well-conducted and include well-designed studies. Cochrane was initiated in 1993 with an aim of conducting high-quality systematic reviews. We aimed to examine the publication rates of non-Cochrane systematic reviews (henceforth referred to simply as "systematic reviews") and Cochrane reviews produced throughout Cochrane's existence and characterize changes throughout the period. METHODS: This observational study collected data on systematic reviews published between 1993 and 2022 in PubMed. Identified Cochrane reviews were linked to data from the Cochrane Database of Systematic Reviews via their Digital Object Identifier. Systematic reviews and Cochrane reviews were analyzed separately. Two authors screened a random sample of records to validate the overall sample, providing a precision of 98%. RESULTS: We identified 231,602 (94%) systematic reviews and 15,038 (6%) Cochrane reviews. Publication of systematic reviews has continuously increased with a median yearly increase rate of 26%, while publication of Cochrane reviews has decreased since 2015. From 1993 to 2002, Cochrane reviews constituted 35% of all systematic reviews in PubMed compared with 3.5% in 2013-2022. Systematic reviews consistently had fewer authors than Cochrane reviews, but the number of authors increased over time for both. Chinese first authors conducted 15% and 4% of systematic reviews published from 2013-2022 and 2003-2012, respectively. Most Cochrane reviews had first authors from the UK (36%). The native English-speaking countries the USA, the UK, Canada, and Australia produced a large share of systematic reviews (42%) and Cochrane reviews (62%). The largest publishers of systematic reviews in the last 10 years were gold open access journals. CONCLUSIONS: Publication of systematic reviews is increasing rapidly, while fewer Cochrane reviews have been published through the last decade. Native English-speaking countries produced a large proportion of both types of systematic reviews. Gold open access journals and Chinese first authors dominated the publication of systematic reviews for the past 10 years. More research is warranted examining why fewer Cochrane reviews are being published. Additionally, examining these systematic reviews for research waste metrics may provide a clearer picture of their utility.
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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 | 0.162 | 0.571 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.075 | 0.093 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".