CE-14 Improved survival in systemic lupus erythematosus: a population-based study
Bibliographic record
Abstract
Background Systemic lupus erythematosus (SLE) is associated with an increased risk of mortality. However, recent mortality trends in SLE are unknown, particularly at the general population level. Our objective was to assess mortality trends among SLE patients between January 1, 1997 and December 31, 2012 in a general population context. Materials and methods Using an administrative health database from the province of British-Columbia, Canada (population ∼ 4.5 million), we identified all incident cases of SLE and up to 10 (3were selected) non-SLE controls matched based on sex, age, and calendar year of study entry, between 1997 and 2012. The SLE cohort was then divided in two cohorts based on year of SLE diagnosis (i.e., 1997–2004 and 2005–2012) to evaluate changes in mortality over time. We calculated hazard ratios (HR) for death using Cox proportional hazard models and the rate difference using an additive hazard model, while additionally adjusting for possible confounders (i.e., Charlson Comorbidity Index, number of outpatient visits, hospitalizations, cardiovascular disease medications, glucocorticoids and NSAIDs at baseline). Results The early cohort (1997–2004) of SLE patients had a considerably higher mortality rate than the late cohort (2005–2012) (i.e., 67.33 cases vs. 25.98 cases per 1000 person-years). In contrast, only a moderate improvement was observed in comparison cohorts between the two periods (11.39 to 7.23 per 1000 person-years, respectively). The corresponding absolute mortality rate differences were 40.3 (95% CI: 33.0, 47.7) and 6.4 (95% CI: 2.9, 9.9) cases per 1000 person years (p-value for interaction < 0.001). The corresponding adjusted HRs for mortality were 3.95 (95% CI: 3.24, 4.83) and 2.41 (95% CI: 2.01, 2.89), respectively (p for interaction < 0.001). Conclusions This population-based study shows that survival of SLE patients has improved over the past decade, suggesting that new treatments and improved management of the disease and its complications may be providing substantial benefits.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".