Magnetic Resonance Imaging Followup of Temporomandibular Joint Inflammation, Deformation, and Mandibular Growth in Juvenile Idiopathic Arthritis Patients Receiving Systemic Treatment
Bibliographic record
Abstract
OBJECTIVE: To investigate the course of temporomandibular joint (TMJ) inflammation, osseous deformation, and mandibular ramus growth in children with juvenile idiopathic arthritis (JIA) during systemic therapy. METHODS: Longitudinal study of 38 consecutive patients with JIA (29 female, median age 9.0 yrs, interquartile range 6.2-10.7 yrs) receiving systemic therapy with TMJ involvement, with 2 TMJ magnetic resonance imaging (MRI) examinations ≥ 2 years apart and no TMJ corticosteroid injection. Clinical and MRI findings were compared between initial and followup examinations and between TMJ with and without active inflammation at baseline. RESULTS: Over a median period of 3.6 years (range, 2.0-8.7 yrs), MRI grade of TMJ inflammation improved (p = 0.009) and overall osseous deformity tended to become less severe (p = 0.114). In TMJ with arthritis at baseline (46 TMJ), both the grades of inflammation (p < 0.001) and deformity (p = 0.011) improved. In TMJ with no arthritis at baseline (30 TMJ), the frequency and grade of condylar deformation remained stable. Mandibular ramus growth rates were not significantly different between TMJ with and without arthritis at baseline (1.3 mm/yr vs 1.5 mm/yr, p = 0.273), and were not correlated with the degree of inflammation at baseline or followup. The frequency of facial asymmetry tended to be lower at followup than at initial examination (24% vs 45%, p = 0.056). CONCLUSION: Our results suggest that systemic treatment of TMJ arthritis in children with JIA decreases the degree of inflammation seen on MRI, preserves osseous TMJ morphology, and maintains normal mandibular ramus growth.
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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) | 0.000 | 0.000 |
| 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".