The role of aggressive corticosteroid therapy in patients with juvenile dermatomyositis: A propensity score analysis
Bibliographic record
Abstract
OBJECTIVE: To compare outcomes at 36 months in patients newly diagnosed with juvenile dermatomyositis (DM) treated with aggressive versus standard therapy. METHODS: At diagnosis, 139 untreated juvenile DM patients were given aggressive therapy (intravenous methylprednisolone or oral prednisone 5-30 mg/kg/day; n = 76) or standard therapy (1-2 mg/kg/day; n = 63) by the treating physician. Aggressive therapy patients were more ill at diagnosis. Matching was based on the propensity for aggressive therapy because propensity scoring can reduce confounding by indication. Logistic regression of the matched data determined predictors of outcomes, controlling for clinical confounders and propensity score. Outcomes comprised Disease Activity Score (DAS) for skin and muscle, range of motion (ROM), and calcification. RESULTS: Sex, race, and age were similar between groups, and initial DAS weakness and ROM significantly predicted the therapy chosen. Based on propensity scores, 42 patients from each group were well matched. In the matched pairs, there were no significant differences in outcomes. Methotrexate use (odds ratio [OR] 3.6, 95% confidence interval [95% CI] 1.15-11.5) and duration of untreated disease (OR 1.2, 95% CI 1-1.38) were associated with ROM loss, hydroxychloroquine use (OR 11.2, 95% CI 3.7-33) and calcification (OR 6.8, 95% CI 1.8-25.4) with persistent rash, abnormal baseline lactate dehydrogenase (OR 11.2, 95% CI 1.4-92) and age at onset (OR 1.3, 95% CI 1-1.4) with weakness, and duration of untreated disease (OR 1.2, 95% CI 1-1.39) with calcification. CONCLUSION: Using a retrospective, nonrandomized design with propensity score matching, there was little difference in efficacy outcomes between aggressive and standard therapy; however, the sickest patients were treated with aggressive therapy and were not included in the matched analysis. Comprehensive clinical studies are needed to determine therapeutic pathways to the best outcome.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".