Discrete Choice Experiment on a Magnetic Resonance Imaging Scoring System for Temporomandibular Joints in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the relative importance weights of items and grades of a newly developed additive outcome measure called the juvenile idiopathic arthritis (JIA) magnetic resonance imaging (MRI) scoring system for the temporomandibular joint (TMJ) (JAMRIS-TMJ). METHODS: An adaptive partial-profile, discrete choice experiment (DCE) survey using the 1000Minds platform was independently completed by members of an expert group consisting of radiologists and non-radiologist clinicians to determine the group-averaged relative weights for the JAMRIS-TMJ. Subsequently, an image-based vignette ranking exercise was done, during which experts individually rank ordered 14 patient vignettes for disease severity while blinded to the weights and unrestricted to JAMRIS-TMJ assessment criteria. Validity of the weighted JAMRIS-TMJ was tested by comparing the consensus-graded, DCE-weighted JAMRIS-TMJ score of the vignettes with their unrestricted image-based ranks provided by the experts. RESULTS: Nineteen experts completed the DCE survey, and 21 completed the vignette ranking exercise. Synovial thickening and joint enhancement showed higher weights per raw score compared to bone marrow items and effusion in the inflammatory domain, while erosions and condylar flattening showed nonlinear and higher weights compared to disk abnormalities in the damage domain. The weighted JAMRIS-TMJ score of the vignettes correlated highly with the ranks from the unrestricted comparison method, with median Spearman's ρ of 0.92 (interquartile range [IQR] 0.87-0.95) for the inflammation and 0.93 (IQR 0.90-0.94) for the damage domain. CONCLUSION: A DCE survey was used to quantify the importance weights of the items and grades of the JAMRIS-TMJ. The weighted score showed high convergent validity with an unrestricted, holistic vignette ranking method.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".