Risk Factors for Temporomandibular Joint Arthritis in Children with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and features of temporomandibular joint (TMJ) arthritis by magnetic resonance imaging (MRI) among children with juvenile idiopathic arthritis (JIA), and to identify risk factors for TMJ arthritis. METHODS: A retrospective chart review was performed on 187 patients with JIA who underwent a TMJ MRI at Children's Hospital of Alabama between September 2007 and June 2010. Demographic and clinical information was abstracted from the charts. Univariate and multivariate analyses were performed to identify risk factors for TMJ arthritis identified by MRI. RESULTS: MRI evidence of TMJ arthritis was detected in 43% of patients, with no significant difference among JIA categories. The number of joints with active arthritis (exclusive of the TMJ) and the use of systemic immunomodulatory therapies were not associated with TMJ arthritis. Multivariable analysis revealed a strong association between mouth-opening deviation and TMJ arthritis (OR 6.21, 95% CI 2.87-13.4). A smaller maximal incisal opening and shorter disease duration were also associated with an increased risk of TMJ arthritis. CONCLUSION: TMJ arthritis was identified in a substantial proportion of children with JIA (43%) and affects all JIA categories. TMJ arthritis was present in some patients despite limited or otherwise quiescent disease and in the presence of concurrent systemic immunomodulatory therapy. Routine evaluation for TMJ arthritis by MRI is warranted for all children with JIA.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".