Epidemiological Characteristics of Patients With Juvenile Dermatomyositis in China: A Multicenter Study
Bibliographic record
Abstract
OBJECTIVE: Juvenile dermatomyositis (JDM) is a rare but chronic autoimmune disease with systemic nonsuppurative inflammation. Many studies have focused on the clinical characteristics and therapy of JDM. Whereas a few studies have reported the epidemiological characteristics and social burden of patients with JDM internationally, no study has been performed in China to date. METHODS: This study was based on the Futang Updating Medical Records (FUTURE) database. Data were extracted from registry information in inpatient medical records. The epidemiological characteristics and economic burden of Chinese patients with JDM were analyzed. RESULTS: A total of 1164 patients with JDM from 24 hospitals were enrolled from January 2016 to December 2021. The ratio of boys to girls was 1:1.23, and half were between 6 and 12 years old. Over half (n = 629) were admitted to the hospital at least twice for intensive treatment. In total, 20.02% of patients with JDM had lung involvement and 2.23% experienced subcutaneous calcification. The median number days of hospitalization was 10 (IQR 6-14), whereas the median hospitalization expense was US $2370.50 (IQR 1373.70-3541.90). Lung involvement was the most frequent complication, causing high inpatient burden, length of stay, and expense. Nearly 17% of patients with JDM were admitted to the hospital as emergencies, suggesting a severe disease activity stage requiring urgent treatment. No deaths occurred in our study. CONCLUSION: In our study, we analyzed the epidemiological characteristics and social burden of patients with JDM in China, contributing to the enhanced comprehension and effective management of JDM in the country.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".