The Effect of Male Sex on Survival in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) has a female predominance, however, little is understood about the effect of sex on SSc manifestations and survival. The objectives of our study were to evaluate differences in disease manifestations, and survival rates between males and females with SSc. METHODS: A retrospective cohort study of the Toronto Scleroderma Program was conducted to evaluate sex-based differences in disease manifestations and survival. A relative survival analysis compared SSc survival to the general population. RESULTS: There were 959 patients (791 females, 168 males) identified, with a female:male ratio of 4.7:1. Males more frequently had diffuse SSc [45% vs 30%, relative risk (RR) 1.44, 95% CI 1.18-1.75] and interstitial lung disease (ILD; 41% vs 33%, RR 1.24, 95% CI 1.01-1.52). There were 324 deaths (65 males, 259 females). Males had increased unadjusted mortality compared to females (HR 1.57, 95% CI 1.19-2.06). In an adjusted model including immunosuppressive use, male sex (HR 1.40, 95% CI 1.06-1.85), ILD (HR 1.58, 95% CI 1.26-1.98), and older age at diagnosis (HR 1.05, 95% CI 1.04-1.06) were independently associated with increased mortality, whereas the limited subtype (HR 0.70, 95% CI 0.49-0.77) and anticentromere antibodies (HR 0.70, 95% CI 0.49-0.98) were independently associated with decreased mortality. Male sex was associated with increased risk of mortality (HR 1.16, p=0.003) in patients with SSc above that observed for males in the general population. CONCLUSION: The differential effect of disease between sexes is small, yet males have decreased survival compared to females with SSc.
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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 | 0.004 |
| 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) | 0.003 | 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".