Early Mortality in a Multinational Systemic Sclerosis Inception Cohort
Bibliographic record
Abstract
OBJECTIVE: To determine mortality and causes of death in a multinational inception cohort of subjects with systemic sclerosis (SSc). METHODS: We quantified mortality as standardized mortality ratio (SMR), years of life lost, and percentage mortality in the first decade of disease. The inception cohort comprised subjects recruited within 4 years of disease onset. For comparison, we used a prevalent cohort, which included all subjects irrespective of disease duration at recruitment. We determined a single primary cause of death (SSc related or non-SSc related) using a standardized case report form, and we evaluated predictors of mortality using multivariable Cox regression. RESULTS: In the inception cohort of 1,070 subjects, there were 140 deaths (13%) over a median follow-up of 3.0 years (interquartile range 1.0-5.1 years), with a pooled SMR of 4.06 (95% confidence interval [95% CI] 3.39-4.85), up to 22.4 years of life lost in women and up to 26.0 years of life lost in men, and mortality in the diffuse disease subtype of 24.2% at 8 years. In the prevalent cohort of 3,218 subjects, the pooled SMR was lower at 3.39 (95% CI 3.06-3.71). In the inception cohort, 62.1% of the primary causes of death were SSc related. Malignancy, sepsis, cerebrovascular disease, and ischemic heart disease were the most common non-SSc-related causes of death. Predictors of early mortality included male sex, older age at disease onset, diffuse disease subtype, pulmonary arterial hypertension, and renal crisis. CONCLUSION: Early mortality in SSc is substantial, and prevalent cohorts underestimate mortality in SSc by failing to capture early deaths, particularly in men and those with diffuse disease.
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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 | 0.002 |
| 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 | 0.001 | 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".