Profile of Juvenile Idiopathic Arthritis Patients in a Specialized Temporomandibular Joint Clinic in Canadian Pediatric Hospitals.
Bibliographic record
Abstract
PURPOSE: A specialized temporomandibular joint (TMJ) dental clinic was created at the Centre hospitalier universitaire (CHU) Sainte-Justine to optimize care of patients with juvenile idiopathic arthritis (JIA). In this article, we characteristics of patients with JIA and the resources available in Canadian pediatric hospitals for JIA patients with TMJ involvement. METHODS: To determine patient characteristics, we compiled retrospective data on patients seen at the TMJ clinic. Regarding resources available for patients with JIA, we sent questionnaires to the departments of rheumatology and dentistry of 13 Canadian pediatric hospitals. RESULTS: Of the 86 JIA patients included in our study, 42% (95% confidence interval 32-52%) had TMJ involvement. Panoramic radiography was the imaging prescribed most often for patients with JIA (91%) and frequency of follow up was most often every 6 months. In the second part of the study, 7 hospitals were included; 2 had a specialized TMJ clinic. In many cases, reports of types of imaging and available dental specialists differed between the rheumatology and dentistry questionnaires for the same hospital. CONCLUSION: Few Canadian pediatric hospitals have a specialized TMJ clinic for JIA, and there seems to be a gap in the knowledge of primary care physicians regarding TMJ diagnosis and the management of patients 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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".