Early Mortality in a Multinational Systemic Sclerosis Inception Cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it