Risk Factors for Intraarticular Heterotopic Bone Formation in the Temporomandibular Joint in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Intraarticular corticosteroid (IAC) injections are often used to treat temporomandibular joint (TMJ) arthritis associated with juvenile idiopathic arthritis (JIA). One potential complication of IA therapy is heterotopic bone formation (HBF). The purpose of our study was to evaluate risk factors for HBF development in children with JIA who received IA therapy for TMJ arthritis. METHODS: This was a retrospective study of children with JIA who had received ≥ 1 IAC injection into the TMJ. Survival regression analysis was performed to identify risk factors for the development of HBF. RESULTS: There were 238 children included, of whom 33 (14%) developed HBF. No cases of HBF were diagnosed prior to the initial injection. Univariate analysis revealed that the risk factors for development of HBF were the total number of injections received into the TMJ and age at diagnosis of JIA, while the length of time from diagnosis of JIA to the first injection was inversely associated with the risk of HBF formation. The total number of injections was no longer significant following adjusted survival models. Children with HBF had increased physical examination evidence of acute or chronic changes, namely decreased maximal incisal opening and increased likelihood of jaw deviation. CONCLUSION: HBF within the TMJ is relatively common in patients with JIA receiving IAC injections for TMJ arthritis. Future prospective studies are required to delineate the risks posed by the injections themselves as opposed to the underlying disease activity, as well as to evaluate alternative forms of local therapy to the TMJ.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".