Mortality Profile Related to Systemic Lupus Erythematosus: A Multiple Cause-of-death Analysis
Bibliographic record
Abstract
OBJECTIVE: To analyze the mortality profile related to systemic lupus erythematosus (SLE) in the state of São Paulo, Brazil. METHODS: For the 1985-2007 period, we analyzed all death certificates (n = 4815) on which SLE was listed as an underlying (n = 3133) or non-underlying (n = 1682) cause of death. We evaluated sex, age, and the causes of death, comparing the first and last 5 years of the period, as well as determining the observed/expected death ratio (O/E ratio). RESULTS: For SLE as an underlying cause, the mean age at death was 35.77 years (SD 15.12) and the main non-underlying causes of death were renal failure, circulatory system diseases, pneumonia, and septicemia. Over the period, the proportional mention of infectious causes and circulatory system diseases increased, whereas renal diseases decreased. For SLE as a non-underlying cause of death, the most common underlying causes of death were circulatory, respiratory, genitourinary, and digestive system diseases, and certain infections. The overall death O/E ratio was > 1 for renal failure, tuberculosis, septicemia, pneumonia, and digestive system diseases, as well as for circulatory system diseases at < 50 years of age, particularly acute myocardial infarct. CONCLUSION: Unlike in developed countries, renal failure and infectious diseases are still the most frequent causes of death. The increase in SLE deaths associated with infection, especially pneumonia and septicemia, is worrisome. The judicious use of immunosuppressive therapy together with vigorous treatment of cardiovascular comorbidities is crucial to the successful management of SLE and to improving survival of patients with SLE.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".