Current State of Reporting Pain Outcomes in Cochrane Reviews of Chronic Musculoskeletal Pain Conditions and Considerations for an OMERACT Research Agenda
Bibliographic record
Abstract
OBJECTIVE: To assess the current state of reporting of pain outcomes in Cochrane reviews on chronic musculoskeletal painful conditions and to elicit opinions of patients, healthcare practitioners, and methodologists on presenting pain outcomes to patients, clinicians, and policymakers. METHODS: We identified all reviews in the Cochrane Library of chronic musculoskeletal pain conditions from Cochrane review groups (Back, Musculoskeletal, and Pain, Palliative, and Supportive Care) that contained a summary of findings (SoF) table. We extracted data on reported pain domains and instruments and conducted a survey and interviews on considerations for SoF tables (e.g., pain domains, presentation of results). RESULTS: Fifty-seven SoF tables in 133 Cochrane reviews were eligible. SoF tables reported pain in 56/57, with all presenting results for pain intensity (20 different outcome instruments), pain interference in 8 SoF tables (5 different outcome instruments), and pain frequency in 1 multiple domain instrument. Other domains like pain quality or pain affect were not reported. From the survey and interviews [response rate 80% (36/45)], we derived 4 themes for a future research agenda: pain domains, considerations for assessing truth, discrimination, and feasibility; clinically important thresholds for responder analyses and presenting results; and establishing hierarchies of outcome instruments. CONCLUSION: There is a lack of standardization in the domains of pain selected and the manner that pain outcomes are reported in SoF tables, hampering efforts to synthesize evidence. Future research should focus on the themes identified, building partnerships to achieve consensus and develop guidance on best practices for reporting pain outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.662 | 0.866 |
| Meta-epidemiology (narrow) | 0.004 | 0.010 |
| Meta-epidemiology (broad) | 0.016 | 0.019 |
| Bibliometrics | 0.060 | 0.056 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.050 | 0.050 |
| Open science | 0.013 | 0.023 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".