Assessing Arthritis in the Temporomandibular Joint
Bibliographic record
Abstract
A variety of characteristics make the temporomandibular joint (TMJ) distinct from other joints: (1) The bony connection with the contralateral TMJ, through the mandible, and the great variety of movement directions and trajectories make movements in this joint extremely complex and render an exact assessment of the range of motion of each single TMJ impossible; (2) the tight temporomandibular ligament, part of the lateral joint capsule, impedes the palpatory assessment of joint swelling or effusion; (3) the joint surface is covered with fibrous cartilage, below which immediately follows a zone of pluripotent proliferating cells promoting the growth of the whole mandible, the bone with the highest growth rate of the head; and (4) although pain from the temporomandibular region is not uncommon, it is highly unspecific and not a reliable indicator for TMJ arthritis1. In this issue of The Journal , Stoll, et al present their study about intraarticular (IA) infliximab therapy for TMJ arthritis in children with juvenile idiopathic arthritis (JIA)2. Because of the lack of convincing treatment strategies for TMJ arthritis, especially in children, this report is of high interest. While for most other joints IA steroid injections are a recommended treatment option3, there is increasing awareness of severe negative effects of steroid injections … Address correspondence to Dr. R.K. Saurenmann, Department of Rheumatology, University Children’s Hospital, Steinwiesstr. 75, Zurich, Switzerland, CH-8032; E-mail: traudel.saurenmann{at}kispi.uzh.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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".