Clinical and Laboratory Characteristics and Mortality in Korean Patients with Systemic Sclerosis: A Nationwide Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: We aimed to investigate demographic and clinical features and predictors of mortality in Korean patients with systemic sclerosis (SSc). METHODS: We performed a retrospective multicenter medical chart review in Korean patients diagnosed with SSc from 1986 to 2016 at 11 university hospitals representing each geographic area of Korea. SSc patients were defined according to the American College of Rheumatology preliminary classification criteria and subtyped as limited cutaneous (lcSSc) or diffuse cutaneous (dcSSc) SSc. RESULTS: We enrolled 751 patients (female, 86.7%; mean age at diagnosis, 48.9 yrs). The most common organ involvement was interstitial lung disease (52.7%), followed by gastroesophageal reflux disease (32.9%) and pulmonary arterial hypertension (13.6%). Patients with lcSSc were more common than those with dcSSc (64.8 vs 35.2%), whereas anti-Scl-70 and anticentromere antibody positivity were identified in 302 (42.5%) and 175 (25.5%) patients, respectively. In the 46 (6.1%) patients who developed a malignancy, lung cancer (23.9%) was the most common diagnosis, followed by gastric (13%) and breast cancer (13%). During the study period, 57 (7.6%) patients died, and the 5- and 10-year survival rates were 94% and 87%, respectively. Increased age at diagnosis, cardiovascular involvement, and anti-Scl-70 antibody positivity were significant predictors of death. CONCLUSION: Clinical manifestations and survival rates in Korean SSc patients are similar to those of other populations. However, the prevalence of anti-Scl-70 antibody is higher in Korean SSc patients compared with whites, while the prevalence of anticentromere antibody is lower.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".