Predicting disease severity and remission in juvenile idiopathic arthritis: are we getting closer?
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize current research on the prediction of severe disease or remission in children with juvenile arthritis, and define further steps needed towards developing prediction tools with sufficient accuracy for clinical use. RECENT FINDINGS: High disease activity, poor patient-reported outcomes, ankle or wrist involvement, and a longer time from onset to the start of treatment herald a severe disease course and a low chance of remission. Other studies confirmed that age less than 7 years and positive ANA are the strongest predictors of uveitis development. Preliminary evidence suggests ultrasound findings may predict flare in patients with clinically inactive disease, and several new biomarkers show promise. A few prediction tools that combine predictors to estimate the chance of remission or a severe disease course in the medium-term to long-term have shown good accuracy when internally validated in the population in which they were developed. SUMMARY: Promising candidate tools for predicting disease severity and long-term remission in juvenile arthritis are now available. These tools need external validation in other populations, and ideally formal trials to assess whether their use in practice improves patient outcomes. We are definitively getting closer, but we are not there yet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".