Can Ultrasound Be Used to Predict Loss of Remission in Patients with RA in a Real-life Setting? A Multicenter Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Several studies have suggested that patients with rheumatoid arthritis (RA) presenting with ultrasound (US) synovitis despite clinical remission have more subsequent flares than those who show both clinical and sonographic remission. The objective of our study was to investigate whether these results could be translated to a real-life setting. METHODS: We compared the time from the first US performed in clinical remission to loss of remission (defined by a DAS28 > 2.6 or the need for stepping up treatment with disease-modifying antirheumatic drugs) within the Swiss Clinical Quality Management cohort of patients with RA, and we adjusted for relevant confounders. Analyses were repeated for different definitions of US-detected synovitis (US+) using greyscale, Doppler, and combined modes based on previously validated scores, and they were adjusted for relevant confounders. RESULTS: There were 318 RA patients with 378 remission phases included. Loss of clinical remission was observed in 60% of remission phases. Residual US synovitis was associated with a shorter duration of clinical remission (median 2-5 mos) and a moderately increased hazard ratio (HR) for loss of remission (HR 1.2-1.5), with the highest HR for the combined US score. The association between US+ and loss of remission was strongest when the US measurement had taken place early in remission (shorter median duration of 6-20 mos) and when followup time was limited to the first 3 or 6 months (most HR between 2-4). CONCLUSION: US-detected synovitis, particularly when US is performed early in clinical remission, has a moderate predictive power for loss of remission in a real-life setting.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".