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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".