Optimal Strategies for Reporting Pain in Clinical Trials and Systematic Reviews: Recommendations from an OMERACT 12 Workshop
Bibliographic record
Abstract
OBJECTIVE: Pain is a patient-important outcome, but current reporting in randomized controlled trials and systematic reviews is often suboptimal, impeding clinical interpretation and decision making. METHODS: A working group at the 2014 Outcome Measures in Rheumatology (OMERACT 12) was convened to provide guidance for reporting treatment effects regarding pain for individual studies and systematic reviews. RESULTS: For individual trials, authors should report, in addition to mean change, the proportion of patients achieving 1 or more thresholds of improvement from baseline pain (e.g., ≥ 20%, ≥ 30%, ≥ 50%), achievement of a desirable pain state (e.g., no worse than mild pain), and/or a combination of change and state. Effects on pain should be accompanied by other patient-important outcomes to facilitate interpretation. When pooling data for metaanalysis, authors should consider converting all continuous measures for pain to a 100 mm visual analog scale (VAS) for pain and use the established, minimally important difference (MID) of 10 mm, and the conventionally used, appreciably important differences of 20 mm, 30 mm, and 50 mm, to facilitate interpretation. Effects ≤ 0.5 units suggest a small or very small effect. To further increase interpretability, the pooled estimate on the VAS should also be transformed to a binary outcome and expressed as a relative risk and risk difference. This transformation can be achieved by calculating the probability of experiencing a treatment effect greater than the MID and the thresholds for appreciably important differences in pain reduction in the control and intervention groups. CONCLUSION: Presentation of relative effects regarding pain will facilitate interpretation of treatment effects.
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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 | 0.676 | 0.749 |
| Meta-epidemiology (narrow) | 0.009 | 0.014 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.019 | 0.022 |
| Research integrity | 0.030 | 0.034 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".