OMERACT Endorsement of Measures of Outcome for Studies of Acute Gout
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which participants at the Outcome Measures in Rheumatology (OMERACT) 11 meeting agree that instruments used in clinical trials to measure OMERACT core outcome domains in acute gout fulfill OMERACT filter requirements of truth, discrimination, and feasibility; and where future research efforts need to be directed. METHODS: Results of a systematic literature review and analysis of individual-level data from recent clinical studies of acute gout were presented to OMERACT participants. The information was discussed in breakout groups, and opinion was defined by subsequent voting in a plenary session. Endorsement was defined as at least 70% of participants voting in agreement with the proposition (where the denominator excluded those participants who did not vote or who voted "don't know"). RESULTS: The following measures were endorsed for use in clinical trials of acute gout: (1) 5-point Likert scale and/or visual analog scale (0 to 100 mm) to measure pain; (2) 4-point Likert scale for joint swelling; (3) 4-point Likert scale for joint tenderness; and (4) 5-point Likert scale for patient global assessment of response to treatment. Measures for the activity limitations domain were not endorsed. CONCLUSION: Measures of pain, joint swelling, joint tenderness, and patient global assessment in acute gout were endorsed at OMERACT 11. These measures should now be used in clinical trials of acute gout.
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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 | 0.384 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".