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 2006-14. 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 subgroups, according to diagnosis year: early cohort 1997-2005 and late cohort 2006-14. The outcome was death [all-cause, renal disease, cancer, infection, cardiovascular disease (CVD) and other]. Hazard ratios (HR) and 95% CIs were estimated using univariate and multivariable Cox models. RESULTS: Among 6092 SLE patients and 60 920 non-SLE individuals, there were 451 and 1910 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) vs 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) vs 2.65 (1.78, 3.93)], infection [2.94 (2.17, 3.98) vs 2.54 (1.84, 3.51)] and CVD [1.95 (1.60, 2.38) vs 2.18 (1.76, 2.71)]. There was no increase in cancer-related mortality in either cohort [1.27 (0.96, 1.69) vs 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 with 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".