Consensus on the Need for a Hierarchical List of Patient-reported Pain Outcomes for Metaanalyses of Knee Osteoarthritis Trials: An OMERACT Objective
Bibliographic record
Abstract
OBJECTIVE: Although protocol registration for systematic reviews is still not mandatory, reviewers should be strongly encouraged to register the protocol to identify the methodological approach, including all outcomes of interest. This will minimize the likelihood of biased decisions in reviews, 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 metaanalyses. METHODS: Multiple outcome measurement instruments exist to measure the same outcome. Metaanalysis 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), the Cochrane Collaboration, 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 2 existing hierarchical lists of pain outcome measurement instruments recommended for metaanalyses of knee OA trials. RESULTS: After discussions, most participants agreed that there is a need to develop a methodology for generation of hierarchical lists of outcome measurement instruments to guide metaanalyses. Tools that could be used to steer development of such a prioritized list are the COSMIN checklist (COnsensus-based Standards for the selection of health status Measurement Instruments) and the OMERACT Filter 2.0. CONCLUSION: We list meta-epidemiological research agenda items that address the frequency of reported outcomes in trials, as well as methodologies to assess the best measurement properties (i.e., truth, discrimination, and feasibility).
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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 | 0.682 | 0.778 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.018 | 0.022 |
| Research integrity | 0.031 | 0.053 |
| Insufficient payload (model declined to judge) | 0.007 | 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".