Clinical and epidemiological features of juvenile-onset systemic sclerosis from a nationwide survey in Japan
Bibliographic record
Abstract
OBJECTIVE: To evaluate clinical and epidemiological features of juvenile-onset systemic sclerosis (jSSc) in Japan and to identify racial and generational differences. METHODS: We surveyed patients with jSSc (developed < 18 years of age) who visited selected facilities in Japan between January 2016 and December 2020. We estimated the number of patients with jSSc and the annual incidence rate in Japan. Thereafter, differences in clinical characteristics by disease subtype, autoantibody, and age at investigation were analyzed and compared with previous cohorts. RESULTS: Of the 3005 institutions selected for the first survey, 1845 (61.4%) responded. The estimated number of patients with jSSc was 299, whereas the estimated annual incidence rate ranged from 0.98 to 1.59 per 1 million children (aged < 18 years) from 2016 to 2020. In the second-stage survey, 130 cases were analyzed, of which 85 (65.4%) had diffuse cutaneous SSc (dcSSc), 77.7% were female, and the median ages at onset and during the survey were 11 and 21 years, respectively. Autoantibody positivity was 62.4% for antitopoisomerase I antibody (ATA) and 12.9% for anticentromere antibody, whereas anti-PM/Scl antibody was very rare. In total, interstitial lung disease was present in 40.8% of patients (predominantly dcSSc and ATA positive), gastrointestinal lesions in 36.9%, pulmonary arterial hypertension in 7.7%, and no renal crisis. CONCLUSION: This is the largest national survey of jSSc characteristics analyzed in detail by autoantibody and disease subtype. Japanese jSSc was characterized by a very high ATA positivity rate. However, the frequency of major organ involvement was similar to previous reports of jSSc in the West.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".