Mortality in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: To examine mortality rates in the largest systemic lupus erythematosus (SLE) cohort ever assembled. METHODS: Our sample was a multisite international SLE cohort (23 centers, 9,547 patients). Deaths were ascertained by vital statistics registry linkage. Standardized mortality ratio (SMR; ratio of deaths observed to deaths expected) estimates were calculated for all deaths and by cause. The effects of sex, age, SLE duration, race, and calendar-year periods were determined. RESULTS: The overall SMR was 2.4 (95% confidence interval 2.3-2.5). Particularly high mortality was seen for circulatory disease, infections, renal disease, non-Hodgkin's lymphoma, and lung cancer. The highest SMR estimates were seen in patient groups characterized by female sex, younger age, SLE duration <1 year, or black/African American race. There was a dramatic decrease in total SMR estimates across calendar-year periods, which was demonstrable for specific causes including death due to infections and death due to renal disorders. However, the SMR due to circulatory diseases tended to increase slightly from the 1970s to the year 2001. CONCLUSION: Our data from a very large multicenter international cohort emphasize what has been demonstrated previously in smaller samples. These results highlight the increased mortality rate in SLE patients compared with the general population, and they suggest particular risk associated with female sex, younger age, shorter SLE duration, and black/African American race. The risk for certain types of deaths, primarily related to lupus activity (such as renal disease), has decreased over time, while the risk for deaths due to circulatory disease does not appear to have diminished.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".