Predicting the course of juvenile dermatomyositis: Significance of early clinical and laboratory features
Bibliographic record
Abstract
OBJECTIVE: Juvenile dermatomyositis (DM) is a rare chronic inflammatory disease of childhood. The clinical course of juvenile DM appears to be variable, and little is known about predictors of the disease course. The aims of this study were to describe the clinical course of juvenile DM and to determine whether early clinical and laboratory features can be used to predict the time to remission and/or the disease course. METHODS: Clinical and laboratory data from a cohort of 84 patients with juvenile DM were prospectively entered into a database (1990-2005). Remission was defined as a clinical state of no active skin rash, weakness, or elevated muscle enzyme levels for 6 months off medication. The disease course was defined as monophasic, polyphasic, or chronic. Data were reviewed at the time of diagnosis and at 3 months and 6 months after the diagnosis to determine predictors of the time to remission and/or the disease course. RESULTS: The median time to remission was 4.67 years. Sixty percent of patients had a chronic course, 37% a monophasic course, and 3% a polyphasic course. The presence of rash (most strongly indicated by Gottron's papules) at 3 months was the earliest predictor of a longer time to remission (relative risk [RR] 0.55 [95% confidence interval (95% CI) 0.37-0.81], P = 0.002). At 6 months, the presence of nailfold abnormalities and rash also predicted a longer time to remission (RR 0.35 [95% CI 0.14-0.74], P = 0.003). We were unable to determine a prediction model of disease course. CONCLUSION: The majority of patients in our cohort had a chronic disease course. The persistence of Gottron's papules and nailfold abnormalities early in the disease course was associated with a longer 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".