Experts Prioritize Osteoarthritis Non-Surgical Interventions From Cochrane Systematic Reviews for Translation Into “Evidence4Equity” Summaries.
Bibliographic record
Abstract
Abstract Objective: Osteoarthritis carries substantial health and socioeconomic burden, which is particularly marked in marginalised groups. It is imperative that practitioners have ready access to summaries of evidence-based interventions for osteoarthritis that incorporate equity considerations. Summaries of systematic reviews can provide this. The present study surveyed experts to inform the selection of interventions to generate Cochrane Evidence4Equity (E4E) summaries. Methods: We identified 29 non-surgical interventions to prioritise. Key findings from these interventions were summarised and provided to 9 experts in the field of osteoarthritis. Expert participants were asked to rate interventions based on feasibility, health system effects, universality, impact on inequities, and priority for translation into equity based E4E summaries. Results: Expert participants rated land-based exercise highest for priority for translation into an E4E summaries. Conclusion: The survey generated information that can be used to direct and support knowledge translation efforts.
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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 | 0.358 | 0.705 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".