Ultrasound for the diagnosis of gout—the value of gout lesions as defined by the Outcome Measures in Rheumatology ultrasound group
Bibliographic record
Abstract
OBJECTIVE: To evaluate ultrasound for diagnosing gout using consensus-based Outcome Measures in Rheumatology ultrasound definitions of gout lesions. METHODS: Ultrasound was performed in patients with clinically suspected gout. Joints (28) and tendons (26) were binarily evaluated for the Outcome Measures in Rheumatology gout lesions-double contour (DC), tophus, aggregates and erosions. Ultrasound assessment was compared with two reference standards: (i) presence of MSU crystals in joint/tophus aspirate (primary outcome) and (ii) ACR/EULAR 2015 gout classification criteria (secondary outcome). Both reference standards were evaluated by rheumatologists blinded to ultrasound findings. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value of each ultrasound lesion against both reference standards were determined. RESULTS: Eighty-two patients (70 men), mean age 62.4 (range 19-88) years, were included. Fifty-seven patients were MSU-positive whereas 25 patients were MSU-negative (no MSU crystals: 23; aspiration unsuccessful: 2). Of these 25 patients, three patients were classified as ACR/EULAR-positive (i.e. totally 60 ACR/EULAR-positive patients). All ultrasound lesions had high sensitivities for gout (0.77-0.95). DC and tophus showed high specificities (0.88-0.95), positive predictive values (0.94-0.98) and accuracies (0.82-0.84) when both reference standards were used. In contrast, low specificities were found for aggregates and erosions (0.32-0.59). Ultrasound of MTP joints for DC or tophus, knee joint for DC and peroneus tendons for tophus was sufficient to identify all MSU-positive patients with ultrasound signs of gout at any location. CONCLUSION: Ultrasound-visualized DC and tophus, as defined by the Outcome Measures in Rheumatology ultrasound group, show high specificities, positive predictive values and accuracies for diagnosing gout and are therefore valid tools in clinical practice.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".