Are cardiovascular events and mortality in patients with systemic lupus erythematosus predictable at diagnosis?
Bibliographic record
Abstract
This editorial refers to ‘Initial disease severity, cardiovascular events and all-cause mortality among patients with systemic lupus erythematosus’, by Daniel Li et al., on pages 495–504. Despite advances in diagnosis, treatment and overall reduction of mortality in SLE over the last decades, mortality and morbidity due to cardiovascular diseases (CVDs) have not significantly improved [1, 2]. Premature atherosclerosis is a major cause of mortality among SLE patients [3, 4]. In this issue of Rheumatology, Li et al. present the results of an analysis of incident SLE cases within a 10-year period from a USA national administrative database. The authors investigated the association between initial SLE severity within the first year of follow-up and future risks of CVD and all-cause mortality. The patients were classified as having mild, moderate or severe SLE. A total of 15 120 incident SLE patients were included, of which 17.5% were classified as severe. Participants had low socio-economic status and a variety of racial-ethnic backgrounds. The authors found an overall annual CVD incidence rate of 16.1 events per 1000 person-years (95% CI 14.9, 17.3) and an annual mortality rate of 10.6 events per 1000 person-years (95% CI 9.7, 11.5). Patients with initially severe SLE had the highest CVD incidence rate, 24.6 events per 1000 person-years (95% CI 20.6, 29.2) and the highest mortality rate, 23.6 events per 1000 person-years (95% CI 20.5, 27.1). The study demonstrated that SLE patients with initially severe disease had increased CVD and mortality risks compared with patients with mild disease [subdistribution hazard ratios (HRsp): 1.64 (95% CI: 1.32, 2.04) and HR: 3.11 (95% CI: 2.49, 3.89), respectively]. The analyses were adjusted for age, sex, race, region and cardiac-specific risk comorbidities.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".