Musculoskeletal Ultrasound and the Assessment of Disease Activity in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
Objective To determine the frequency of subclinical synovitis on musculoskeletal ultrasonography (MSUS) in juvenile idiopathic arthritis (JIA) and correlate patient‐ and provider‐reported outcome measures with MSUS synovitis. Method JIA patients with an active joint count (AJC) of >4 underwent a 42‐joint MSUS performed at baseline and 3 months. B‐mode and power Doppler images were obtained and scored (range 0–3) for each of the 42 joints. Outcomes evaluated included physician global assessment of disease activity (PhGA), patient global assessment of disease activity (PtGA), patient pain, Childhood Health Assessment Questionnaire (C‐HAQ), and AJC. Subclinical synovitis was defined as synovitis detected by MSUS only. Generalized estimation equations were used to test the relationship between clinical arthritis (positive/negative) and subclinical synovitis (positive/negative). Spearman's correlation coefficients (rs) were calculated to determine the association between MSUS synovitis and patient‐ and physician‐reported outcomes. Results In 30 patients, subclinical synovitis was detected in 30% of joints. Clinical arthritis of the fingers, wrists, and knee joints was significantly associated with MSUS synovitis in these joints. PtGA and the C‐HAQ had a moderate (rs = 0.44, P = 0.014) to weak (rs = 0.37, P = 0.045) correlation with MSUS synovitis. There was a statistically significant strong correlation between MSUS synovitis and PhGA (rs = 0.61, P = 0.001), but a weak correlation with AJC (rs = 0.37, P = 0.048) at the follow‐up visit. Conclusion Subclinical synovitis was commonly observed in this cohort of JIA patients. The fair‐to‐moderate correlation of MSUS synovitis with patient‐ and provider‐reported outcomes suggests that MSUS assesses a different, possibly more objective, domain not determined by traditional JIA outcome measurements.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".