Ultrasound as an Outcome Measure in Gout. A Validation Process by the OMERACT Ultrasound Working Group
Bibliographic record
Abstract
OBJECTIVE: To summarize the work performed by the Outcome Measures in Rheumatology (OMERACT) Ultrasound (US) Working Group on the validation of US as a potential outcome measure in gout. METHODS: Based on the lack of definitions, highlighted in a recent literature review on US as an outcome tool in gout, a series of iterative exercises were carried out to obtain consensus-based definitions on US elementary components in gout using a Delphi exercise and subsequently testing these definitions in static images and in patients with proven gout. Cohen's κ was used to test agreement, and values of 0-0.20 were considered poor, 0.20-0.40 fair, 0.40-0.60 moderate, 0.60-0.80 good, and 0.80-1 excellent. RESULTS: With an agreement of > 80%, consensus-based definitions were obtained for the 4 elementary lesions highlighted in the literature review: tophi, aggregates, erosions, and double contour (DC). In static images interobserver reliability ranged from moderate to almost perfect, and similar results were found for the intrareader reliability. In patients the intraobserver agreement was good for all lesions except DC (moderate). The interobserver agreement was poor for aggregates and DC but moderate for the other components. CONCLUSION: These first steps in evaluating the validity of US as an outcome measure for gout show that the reliability of the definitions ranged from moderate to excellent in static images and somewhat lower in patients, indicating that a standardized scanning technique may be needed, before testing the responsiveness of those definitions in a composite US score.
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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 | 0.476 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".