Temporomandibular Joint Involvement in Children with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the rate of temporomandibular joint (TMJ) involvement and find factors associated with TMJ arthritis in a single-center cohort of patients with juvenile idiopathic arthritis (JIA). METHODS: Retrospective analysis of all patients with JIA visiting the rheumatology clinic between January 1, 2005, and December 31, 2006. Followup information was included until August 2008. A diagnosis of TMJ arthritis was based on clinical rheumatological and/or radiological findings. RESULTS: After a mean followup time for JIA of 4.6 years (range 0.08-14.17), 86/223 patients (38.6%) had developed TMJ arthritis. The rate of TMJ involvement differed significantly among JIA subtypes (p = 0.0016), with 61% in extended oligoarticular, 52% in polyarticular rheumatoid factor (RF)-negative, 50% in psoriatic, 36% in systemic, 33% in polyarticular RF-positive, 33% in persistent oligoarticular, 30% in unclassified JIA, and 11% in enthesitis-related arthritis. The rate of TMJ involvement in our cohort was statistically significantly lower for patients who were HLA-B27-positive (p = 0.0002). In a multivariate analysis, the association of the following factors was confirmed: JIA subtype (p = 0.0001), a higher erythrocyte sedimentation rate (ESR) at diagnosis (p = 0.0038), involvement of joints of the upper extremity (p = 0.011), the absence of HLA-B27 (p = 0.023), and younger age at onset of JIA (p = 0.050). CONCLUSION: In our cohort of children with JIA, the overall rate of TMJ involvement was 38.6%. Patients with certain JIA subtypes, a higher ESR at disease onset, involvement of upper extremity joints, and younger age at diagnosis were more likely to develop TMJ arthritis. The presence of HLA-B27 seemed to be protective.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".