Toward Establishing a Standardized Magnetic Resonance Imaging Scoring System for Temporomandibular Joints in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: The temporomandibular joints (TMJs) are frequently affected in children with juvenile idiopathic arthritis (JIA). Early detection is challenging, as major variation is present in scoring TMJ pathology on magnetic resonance imaging (MRI). Consensus-driven development and validation of an MRI scoring system for TMJs has important clinical utility in timely improvement of diagnosis and serving as an outcome measure. We report on a multi-institutional collaboration toward developing a TMJ MRI scoring system for JIA. METHODS: Seven readers independently assessed MRI scans from 21 patients (42 TMJs, from patients ages 6-16 years) using 3 existing MRI scoring systems from American, German, and Swiss institutions. Reliability scores, scoring system definitions, and items were discussed among 10 JIA experts through 2 rounds of Delphi surveys, nominal group voting, and subsequent consensus meetings to create a novel TMJ MRI scoring system. RESULTS: Average-measure absolute agreement intraclass correlation coefficients (avICCs) for the total scores of all 3 scoring systems were highly reliable at 0.96 each. Osteochondral items showed higher reliability than inflammatory items. An additive system was deemed preferable for assessing minor joint changes over time. Eight items were considered sufficiently reliable and/or important for integration into the consensus scoring system: bone marrow edema and enhancement (avICC 0.57-0.61, smallest detectable difference [SDD] ± 45-63% prior to redefining), condylar flattening (avICC 0.95-0.96, SDD ± 23-28%), effusions (avICC 0.85-0.88, SDD ± 25-26%), erosions (avICC 0.94, SDD ± 20%), synovial enhancement and thickening (previously combined, avICC 0.90-0.91, SDD ± 33%), and disk abnormalities (avICC 0.90, SDD ± 19%). CONCLUSION: A novel TMJ MRI scoring system was developed by consensus. Further iterative refinements and reliability testing are warranted in upcoming studies.
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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 | 0.060 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".