Development and External Validation of a Model Predicting <scp>New‐Onset</scp> Chronic Uveitis at Different Disease Durations in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To develop and externally validate a prediction model for new-onset chronic uveitis in children with juvenile idiopathic arthritis (JIA) for clinical application. METHODS: Data from the international Pharmachild registry were used to develop a multivariable Cox proportional hazards model. Predictors were selected by backward selection, and missing values were handled by multiple imputation. The model was subsequently validated and recalibrated in 2 inception cohorts: the UK Childhood Arthritis Prospective Study (CAPS) study and the German Inception Cohort of Newly diagnosed patients with juvenile idiopathic arthritis (ICON) study. Model performance was evaluated by calibration plots and C statistics for the 2-, 4-, and 7-year risk of uveitis. A diagram and digital risk calculator were created for use in clinical practice. RESULTS: A total of 5,393 patients were included for model development, and predictor variables were age at JIA onset (hazard ratio [HR] 0.83 [95% confidence interval (95% CI) 0.77-0.89]), ANA positivity (HR 1.59 [95% CI 1.06-2.38]), and International League of Associations for Rheumatology category of JIA (HR for oligoarthritis, psoriatic arthritis, and undifferentiated arthritis versus rheumatoid factor-negative polyarthritis 1.40 [95% CI 0.91-2.16]). Performance of the recalibrated prediction model in the validation cohorts was acceptable; calibration plots indicated good calibration and C statistics for the 7-year risk of uveitis (0.75 [95% CI 0.72-0.79] for the ICON cohort and 0.70 [95% CI 0.64-0.76] for the CAPS cohort). CONCLUSION: We present for the first time a validated prognostic tool for easily predicting chronic uveitis risk for individual JIA patients using common clinical parameters. This model could be used by clinicians to inform patients/parents and provide guidance in choice of uveitis screening frequency and arthritis drug therapy.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".