Effects of Biologics on Temporomandibular Joint Inflammation in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
Objective This prospective study investigates the efficacy of biologics in combination with methotrexate (MTX) or leflunomide (LEF) on juvenile idiopathic arthritis (JIA)-related temporomandibular joint (TMJ) arthritis measured by magnetic resonance imaging (MRI)-based inflammation score and deformity score. Methods A prospective, single-center observational cohort study of 18 consecutive patients was performed between September 2018 and April 2023. Inclusion criteria were (1) diagnosis of JIA, (2) MRI-verified TMJ arthritis leading to treatment with tumor necrosis factor inhibitor (TNFi), (3) MRI at 6 months and 24 months after treatment initiation, and (4) clinical follow-up together with an MRI by a pediatric rheumatologist and an orthodontist. Results We included 18 patients (89% female). At the time of the first MRI, median age was 13.2 years (IQR 9.9-17.4), median disease duration was 7.8 years (IQR 3.4-11.1), and 4 received MTX or LEF. During the observation period, significant improvements were observed in TMJ movement pain (P= 0.01), morning stiffness (P= 0.004), opening capacity (P= 0.03), and maximal incisal openingP= 0.006). The inflammation score decreased significantly from a median of 2 (IQR 1-3) at baseline to a median of 1 (IQR 0-2) at 24 months (P= 0.009). In 17 of 36 TMJs (47%), the deformity score improved or remained stable and no significant increase in the median score was observed. Conclusion This is the first prospective, observational study with evidence to support that the orofacial signs, symptoms, and MRI-derived inflammation score in TMJ arthritis can be reduced by treatment with TNFi.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".