Consensus on the need for a hierarchical list of patient-reported pain outcomes for meta-analyses of knee osteoarthritis trials
Bibliographic record
Abstract
The selection of appropriate outcomes is crucial when designing, and subsequently interpreting clinical trials, in order to directly compare the effects of different interventions in ways that minimize bias. The same is likely to apply for systematic reviews and meta-analyses. Although protocol registration for systematic reviews is still not mandatory, reviewers should be strongly encouraged to register the protocol, in order to identify - a priori - the proposed methodological approach, including all outcomes of interest. This will help to minimize the likelihood of biased post hoc decisions in review methods, such as selective outcome reporting. A group of international experts convened to address issues regarding the need to develop hierarchical lists of outcome measurement instruments for a particular outcome for meta-analyses. Meta-analysis of knee osteoarthritis (OA) trials, and the assessment of pain as an outcome, was used as an exemplar to assess how ‘Outcome Measures in Rheumatology’ (OMERACT) and other international initiatives might contribute in this area. The meeting began with formal presentations of background topics, empirical evidence from the literature, and a brief introduction to two existing hierarchical lists of pain outcome measures recommended for meta-analyses of knee OA trials. After discussions most participants agreed that there is a need to develop a methodology for generation of hierarchical lists of outcome instruments for use to guide meta-analyses. Tools that could be used to steer development of such a prioritized list are the COSMIN checklist and the OMERACT filter 2.0. For future research we suggest that among outcome instruments frequently reported in trials for the same domain, those with the best measurement properties (e.g., validity and reliability) would achieve high, if not the highest rankings for use on a hierarchical list.
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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 | 0.701 | 0.786 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.020 | 0.015 |
| Research integrity | 0.020 | 0.059 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".