Reliability of Clinical Symptoms in Diagnosing Temporomandibular Joint Arthritis in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Temporomandibular joint (TMJ) arthritis, commonly considered oligoarthritic/asymptomatic, occurs frequently in children with juvenile idiopathic arthritis (JIA), and gadolinium-enhanced magnetic resonance imaging (Gd-MRI) has proved to be a sensitive diagnostic tool in this context. We compared the reliability of clinical examinations to Gd-MRI results in diagnosing the condition. METHODS: Patients with JIA (134 consecutive) underwent routine clinical and Gd-MRI examinations. The clinical items examined were clicking, tenderness (TMJ/adjacent muscles), and mouth-opening capacity. Blinded MRI reading focused on inflammation (synovitis/hypertrophy). After statistical power analysis, the clinical findings for 134 healthy controls were included. Contingency analysis was used to determine the sensitivity, specificity, and frequency of clinical symptoms (JIA/healthy controls); Cohen's κ was used to establish the interrater reliability. RESULTS: Statistically significant differences were observed between JIA and healthy control groups with regard to the concise screening items (power analysis > 0.95), whereas no differences in mouth-opening capacity were noted. In 80% of the patients with JIA, Gd-MRI revealed signs of TMJ arthritis, with positive correlations between concise screening items and Gd-MRI results. The average specificity was 0.81, but the sensitivity was low, at 0.42. Combining items led to a marked increase in the sensitivity (0.73). There was a high rate of both false-negative and false-positive results (corresponding to clinical underdiagnosis or overdiagnosis of TMJ arthritis). CONCLUSION: Despite a relatively high specificity, clinical examination alone does not seem sufficiently sensitive to adequately detect TMJ arthritis. Thus, a relatively high number of cases will be missed or overdiagnosed, potentially leading to undertreatment or overtreatment. Gd-MRI may support correct diagnosis, thereby helping to prevent undertreatment or overtreatment.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".