Production and citation of cochrane systematic reviews: a bibliometrics analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the production and utilization of Cochrane systematic reviews(CSRs) and to analyze its influential factors, so as to improve the capacity of translating CSRs into practice. METHODS: All CSRs and protocols were retrieved from the Cochrane Library ISSUE 2, 2011 and citation data were retrieved from SCI database. Citation analysis was used to analyze the situation of CSRs production and utilization. RESULTS: CSR publication had grown from an annual average of 32 to 718 documents. Only one developing country was among the ten countries with the largest amount of publications. High income countries accounted for 83% of CSR publications and 90.8% of cited counts. 34.7% of CSRs had a cited count of 0, while only 0.9% had been cited more than 50 times. Highly cited CSRs were published in England, Australia, Canada, USA and other high income countries. The countries with a Cochrane center or a Cochrane methodology group had a greater capability of CSRs production and citing than others. The CSRs addressing the topics of diseases were more than those targeted at public health issues. There was a big gap in citations of different interventions even for the same topic. CONCLUSION: The capability of CSR production and translation grew rapidly, but varied among countries and institutions, which was affected by several factors such as the capability of research, the resourcesand the applicability of the evidence. It is important to improve evidence translation through educating, training and prioritizing the problems based on real demands of end user. This article is protected by copyright. All rights reserved.
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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 | 0.219 | 0.719 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.329 | 0.382 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 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".