Management of Temporomandibular Joint Arthritis in JIA: Tradition-based or Evidence-based?
Bibliographic record
Abstract
Only a decade ago, the temporomandibular joint (TMJ) was referred to as “the forgotten joint in juvenile idiopathic arthritis (JIA)” owing to a lack of clinical and research attention1. More recently, with the finding that TMJ arthritis is seen in a majority of patients with JIA2, an increasing amount of research has focused on this joint. TMJ arthritis may cause pain, impair joint and masticatory function, and affect dentofacial growth, leading to facial asymmetry and bite disturbances. TMJ destruction from arthritis significantly harms oral health and general quality of life3. Despite progress in research, strategies for management of TMJ arthritis are still based primarily on conviction and tradition rather than science. Systemic therapy is frequently ineffective in controlling TMJ disease4. By tradition, local management with intraarticular corticosteroid injection (IACI) is a common backup plan5,6. This has evolved from the decades-long effective use of IACI in management of adult temporomandibular disorder7. In early translation of this technique to children with JIA, it was found to be safe and effective8. IACI improves TMJ/mandibular function and provides pain relief for patients with JIA9. Further study, however, has found that the clinical benefits of IACI usually dissipate within the … Address correspondence to Dr. P. Stoustrup, Vennelyst Blvd. 9-11, 8000 Aarhus C, Denmark. E-mail: pstoustrup{at}odont.au.dk
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".