Update of Strategies to Translate Evidence from Cochrane Musculoskeletal Group Systematic Reviews for Use by Various Audiences
Bibliographic record
Abstract
For rheumatology research to have a real influence on health and well-being, evidence must be tailored to inform the decisions of various audiences. The Cochrane Musculoskeletal Group (CMSG), one of 53 groups of the not-for-profit international Cochrane Collaboration, prepares, maintains, and disseminates systematic reviews of treatments for musculoskeletal diseases. While systematic reviews provided by the CMSG fill a major gap in meeting the need for high-quality evidence syntheses, our work does not end at the completion of a review. The term "knowledge translation" (KT) refers to the activities involved in bringing research evidence to various audiences in a useful form so it can be used to support decision making and improve practices. Systematic reviews give careful consideration to research methods and analysis. Because the review is often long and detailed, the clinically relevant results may not be apparent or in the optimal form for use by patients and their healthcare practitioners. This paper describes 10 formats, many of them new, for ways that evidence from Cochrane Reviews can be translated with the intention of meeting the needs of various audiences, including patients and their families, practitioners, policy makers, the press, and members of the public (the "5 Ps"). Current and future knowledge tools include summary of findings tables, patient decision aids, plain language summaries, press releases, clinical scenarios in general medical journals, frequently asked questions (Cochrane Clinical Answers), podcasts, Twitter messages, Journal Club materials, and the use of storytelling and narratives to support continuing medical education. Future plans are outlined to explore ways of improving the influence and usefulness of systematic reviews by providing results in formats suitable to our varied audiences.
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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 | 0.347 | 0.694 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.058 | 0.032 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".