Application of the OMERACT Filter to Measures of Core Outcome Domains in Recent Clinical Studies of Acute Gout
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which instruments that measure core outcome domains in acute gout fulfill the Outcome Measures in Rheumatology (OMERACT) filter requirements of truth, discrimination, and feasibility. METHODS: Patient-level data from 4 randomized controlled trials of agents designed to treat acute gout and 1 observational study of acute gout were analyzed. For each available measure, construct validity, test-retest reliability, within-group change using effect size, between-group change using the Kruskall-Wallis statistic, and repeated measures generalized estimating equations were assessed. Floor and ceiling effects were also assessed and minimal clinically important difference was estimated. These analyses were presented to participants at OMERACT 11 to help inform voting for possible endorsement. RESULTS: There was evidence for construct validity and discriminative ability for 3 measures of pain [0 to 4 Likert, 0 to 10 numeric rating scale (NRS), 0 to 100 mm visual analog scale (VAS)]. Likewise, there appears to be sufficient evidence for a 4-point Likert scale to possess construct validity and discriminative ability for physician assessment of joint swelling and joint tenderness. There was some evidence for construct validity and within-group discriminative ability for the Health Assessment Questionnaire as a measure of activity limitations, but not for discrimination between groups allocated to different treatment. CONCLUSION: There is sufficient evidence to support measures of pain (using Likert, NRS, or VAS), joint tenderness, and swelling (using Likert scale) as fulfilling the requirements of the OMERACT filter. Further research on a measure of activity limitations in acute gout clinical trials is required.
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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 | 0.315 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".