Clinical Diagnosis of Temporomandibular Joint Arthritis: A Difficult Task
Bibliographic record
Abstract
Juvenile idiopathic arthritis (JIA) frequently involves the temporomandibular joint (TMJ). TMJ involvement was already mentioned as a typical manifestation in the original publication by Sir Frederic Still1. Depending on the methods used, a rate of up to 93% of JIA patients with early inflammatory signs on TMJ imaging have been reported2,3,4,5. The rates reported for TMJ deformation and important mandibular growth disturbance are only slightly lower with 41–78%6,7,8,9,10. Thus, early diagnosis and timely treatment of TMJ arthritis is important in efforts to prevent such irreversible damage. A number of factors contribute to the difficulties connected to the diagnosis of TMJ arthritis: Severity of consequences. The TMJ is a rather small joint and therefore may be regarded of minor importance. However, it has some unique features: movements in the TMJ are of high complexity, and the mechanical load during chewing and biting is higher than in any other joint of the body. Another peculiarity is that the growth of the mandible during childhood originates from a thin layer of growth cartilage cells located just beneath the joint surface11. Therefore, TMJ arthritis in children can not only lead to deformity and destruction of the mandibular head but can also severely affect the growth and development of the whole mandible. Especially in cases with early onset of TMJ arthritis, the resulting growth failure of the mandible will lead to hypognathism and retrognathism and may have important esthetic and functional consequences, the most severe form being a … Address correspondence to Dr. Saurenmann; E-mail: traudel.saurenmann{at}ksw.ch
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".