Predictors of disease course and remission in systemic juvenile idiopathic arthritis: Significance of early clinical and laboratory features
Bibliographic record
Abstract
OBJECTIVE: To determine whether the disease course in systemic juvenile idiopathic arthritis (JIA) can be characterized as monophasic, polycyclic, or persistent, and to determine whether early clinical and laboratory characteristics can be used to predict the disease course and time to remission. METHODS: Forty-five children with systemic JIA diagnosed between 1996 and 2000 were followed up with a standardized data collection protocol, including data on clinical and laboratory features (mean followup 4.9 years). Disease was considered inactive if the clinical and laboratory features were normal. Three definitions of remission were applied to classify disease course. Predictors of disease course were evaluated using multiple logistic regression. Predictors of time to remission were evaluated using Cox proportional hazards regression. RESULTS: When applying a definition of remission requiring inactive disease while not receiving any medications for a period of 3 months, 42.2%, 6.7%, and 51.1% of the patients were classified as having monophasic, polycyclic, and persistent disease, respectively. Fever and active arthritis at 3 months (R2 = 0.42, area under the receiver operating characteristics curve [AUC] = 0.76) and an erythrocyte sedimentation rate (ESR) >26 mm/hour and corticosteroid use at 6 months (R2 = 0.49, AUC = 0.92) were predictive of a nonmonophasic course. Absence of active arthritis, an ESR of <26 mm/hour, and no requirement for corticosteroid therapy at 3 and 6 months were predictors of an earlier time to remission. CONCLUSION: The disease course in systemic JIA can be characterized as monophasic, polycyclic, or persistent using a definition of remission requiring 3 months of inactive disease while not receiving any therapy. Features at 3 and 6 months are predictive of the disease course and time to remission.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".