Predictors of Survival and Causes of Death in Japanese Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To clarify the mortality rates, causes of death, and contributing clinical factors in Japanese patients with systemic sclerosis (SSc). METHODS: A cohort of 405 patients with SSc, who attended our institution during the period 1973 to 2008, was retrospectively analyzed until the end of 2009. Clinical data were obtained from medical records or autopsy reports. RESULTS: The 405 patients with SSc consisted of 310 (76.5%) survivors, 86 (21.2%) who died, and 9 who were lost to followup. Diffuse cutaneous SSc and involvement of organs other than the gastrointestinal tract were more frequent in patients who died, and were associated with a worse prognosis according to Kaplan-Meier analysis. Female sex, limited cutaneous SSc, anticentromere antibody (ACA), and overlap with Sjögren's syndrome (SS) were factors favoring a better prognosis, while overlap with myositis contributed to a poor prognosis. The overall 10-year survival rate was 88%. The patients with SSc had a significantly higher mortality than the general population (standardized mortality ratio 2.76), but the patients with ACA or overlapping SS did not. The most common causes of death were unknown ones including sudden death, followed by malignancy and infection. In patients with pulmonary arterial hypertension, sudden death was the most common cause of mortality. CONCLUSION: The overall mortality rate of patients with SSc was higher than that of the general population, probably because of poor prognostic factors including organ involvement. These factors should be carefully monitored during followup.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".