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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".