All-cause and Cause-specific Mortality in Systemic Lupus Erythematosus : A Population-based Study
Bibliographic record
Abstract
Objective: To investigate all-cause and cause-specific mortality in SLE patients between two time periods, 1997-2005 and 2006-2014. Methods: We used an administrative health database from the province of British Columbia, Canada to match all incident SLE patients to 10 non-SLE individuals on sex, age and index date. Cohorts were divided into two sub-groups, according to diagnosis year: early cohort 1997-2005 and late cohort 2006-2014. The outcome was death (all-cause, renal disease, cancer, infection, cardiovascular disease (CVD), and other). Hazard ratios (HR and 95% confidence intervals) were estimated using univariate and multivariable Cox models. Results: Among 6,092 SLE patients and 60,920 non-SLE individuals, there were 451 and 1,910 deaths, respectively. The fully-adjusted all-cause mortality HR (95% CI) in the overall SLE cohort was 1.85 (1.66-2.06), with no statistically significant improvement between early and late cohorts (1.95 (1.69-2.26) versus 1.74 (1.49-2.04). There was excess mortality from renal disease (3.04 (2.29-4.05)), infections (2.74 (2.19-3.43)) and CVD (2.05 (1.77-2.38)), but not cancer (1.18 (0.96-1.46)), in the overall SLE cohort. There was no statistically significant improvement in cause-specific mortality between early and late cohorts for renal disease (3.57 (2.37-5.36) versus 2.65 (1.78-3.93)), infection (2.94 (2.17-3.98) versus 2.54 (1.84-3.51)), and CVD (1.95 (1.60-2.38) versus 2.18 (1.76-2.71)). There was no increase in cancer-related mortality in either cohort (1.27 (0.96-1.69) versus 1.10 (0.82-1.48)). Conclusion: This population-based study demonstrates a persisting mortality gap in all-cause and cause-specific deaths in SLE patients, compared to the general population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".