Experts prioritize osteoarthritis non-surgical interventions from Cochrane systematic reviews for translation into “Evidence4Equity” summaries
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis generates substantial health and socioeconomic burden, which is particularly marked in marginalized 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 prioritize a selection ofinterventions, from which equity focused summaries will be generated. Specifically, the prioritized interventions will be developed into Cochrane Evidence4Equity (E4E) summaries. METHODS: Twenty-seven systematic reviews of OA interventions were found. From these, twenty-nine non-surgical treatments for osteoarthritis were identified, based on statistically significant findings for desired outcome variables or adverse events. Key findings from these studies 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. Expert participants were also encouraged to make comments to provide context for each rating. Free text responses were coded inductively and grouped into subthemes and themes. RESULTS: Expert participants rated the intervention home land-based exercise for knee OA highest for priority for translation into an E4E summaries, followed by the interventions individual land-based exercise for knee OA, class land-based exercise for knee OA, exercise for hand OA and land-based exercise for hip OA. Upon qualitative analysis of the expert participants' comments, fifteen subthemes were identified and grouped into three overall themes: (1) this intervention or an aspect of this intervention is unnecessary or unsafe; (2) this intervention or an aspect of this intervention may increase health inequities; and (3) experts noted difficulties completing rating exercise. CONCLUSION: The list of priority interventions and corresponding expert commentary generated information that will 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 | 0.361 | 0.721 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".