Overall and Cause‐Specific Mortality in Patients With Systemic Lupus Erythematosus: A Meta‐Analysis of Observational Studies
Bibliographic record
Abstract
OBJECTIVE: To determine the magnitude of risk from all-cause and cause-specific mortality in patients with systemic lupus erythematosus (SLE) compared to the general population through a meta-analysis of observational studies. METHODS: We searched the Medline and Embase databases from their inception to October 2011. Observational studies that met the following criteria were assessed: 1) a prespecified SLE definition; 2) overall and/or cause-specific deaths, including cardiovascular disease (CVD), infections, malignancy, and renal disease; and 3) reported standardized mortality ratios (SMRs) and 95% confidence intervals (95% CIs). We calculated weighted-pooled summary estimates of SMRs (meta-SMRs) for all-cause and cause-specific mortality using the random-effects model and tested for heterogeneity using the I(2) statistic by using Stata/IC statistical software. RESULTS: We identified 12 studies comprising 27,123 patients with SLE (4,993 observed deaths) that met the inclusion criteria. Overall, there was a 3-fold increased risk of death in patients with SLE (meta-SMR 2.98, 95% CI 2.32-3.83) when compared with the general population. The risks of death due to CVD (meta-SMR 2.72, 95% CI 1.83-4.04), infection (meta-SMR 4.98, 95% CI 3.92-6.32), and renal disease (SMR 7.90, 95% CI 5.50-11.00) were significantly increased. Mortality due to malignancy was the only cause-specific entity not increased in SLE (meta-SMR 1.19, 95% CI 0.89-1.59). CONCLUSION: The published data indicated a 3-fold increase in all-cause mortality in patients with SLE compared to the general population. Additionally, all cause-specific mortality rates were increased except for malignancy, with renal disease having the highest mortality risk.
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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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.065 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".