Early diagnosis of temporomandibular joint involvement in juvenile idiopathic arthritis: a pilot study comparing clinical examination and ultrasound to magnetic resonance imaging
Bibliographic record
Abstract
OBJECTIVES: To study the validity of both rheumatological and orthodontic examinations and ultrasound (US) as screening methods for early diagnosis of TMJ arthritis against the gold standard MRI. METHODS: Thirty consecutive juvenile idiopathic arthritis (JIA) patients were included in this pilot study. Rheumatological and orthodontic examinations as well as US were performed within 1 month of the MRI in a blinded fashion. Joint effusion and/or increased contrast enhancement of synovium or bone were considered signs of active arthritis on MRI. RESULTS: A total of 19/30 (63%) patients and 33/60 (55%) joints had signs of TMJ involvement on MRI. This was associated with condylar deformity in 9/19 (47%) patients and 15/33 (45%) joints. Rheumatological, orthodontic and US examinations correctly diagnosed 11 (58%), 9 (47%) and 6 (33%) patients, respectively, with active TMJ arthritis, but misdiagnosed 8 (42%), 10 (53%) and 12 (67%) patients, respectively, as having no signs of inflammation. The best predictor for active arthritis on MRI was a reduced maximum mouth opening. CONCLUSION: None of the methods tested was able to reliably predict the presence or absence of MRI-proven inflammation in the TMJ in our cohort of JIA patients. US was the least useful of all methods tested to exclude active TMJ arthritis.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".