Assessing Elementary Lesions in Gout by Ultrasound: Results of an OMERACT Patient-based Agreement and Reliability Exercise
Bibliographic record
Abstract
OBJECTIVE: To test the reliability of the consensus-based ultrasound (US) definitions of elementary gout lesions in patients. METHODS: Eight patients with microscopically proven gout were evaluated by 16 sonographers for signs of double contour (DC), aggregates, erosions, and tophi in the first metatarsophalangeal joint and the knee bilaterally. The patients were examined twice using B-mode US to test agreement and inter- and intraobserver reliability of the elementary components. RESULTS: The prevalence of the lesions were DC 52.8%, tophus 61.1%, aggregates 29.8%, and erosions 32.4%. The intraobserver reliability was good for all lesions except DC, where it was moderate. The best reliability per lesion was seen for tophus (κ 0.73, 95% CI 0.61-0.85) and lowest for DC (κ 0.53, 95% CI 0.38-0.67). The interobserver reliability was good for tophus and erosions, but fair to moderate for aggregates and DC, respectively. The best reliability was seen for erosions (κ 0.74, 95% CI 0.65-0.81) and lowest for aggregates (κ 0.21, 95% CI 0.04-0.37). CONCLUSION: This is the first step to test consensus-based US definitions on elementary lesions in patients with gout. High intraobserver reliability was found when applying the definition in patients on all elementary lesions while interobserver reliability was moderate to low. Further studies are needed to improve the interobserver reliability, particularly for DC and aggregates.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".