Predictors of Longterm Mortality in Patients with and without Systemic Lupus Erythematosus on Maintenance Dialysis: A Comparative Study
Bibliographic record
Abstract
OBJECTIVE: To compare the prognosis of patients with and without systemic lupus erythematosus (SLE) on dialysis and to determine the factors that affect survival after dialysis. METHODS: We used the Taiwan National Health Insurance Research Database (NHRI-NHIRD-99182) and collected data on patients who started maintenance dialysis between 2001 and 2003. Patients were followed from the initiation of dialysis until death, discontinuation of dialysis, or the end of 2008. We did a Kaplan-Meier analysis of the cohort and used multivariate Cox regression analysis to identify significant predictors of survival. RESULTS: Of the 22,394 dialysis patients studied, 303 (1.35%) had SLE. Hypertension and diabetes were the 2 most common comorbidities associated with dialysis for patients with and without SLE. After adjusting for age, sex, dialysis modality, and comorbidities, we found no significant survival difference between the 2 patient groups after 8 years of followup. Multivariate analysis showed that increased mortality in the patient group without SLE (p < 0.05) was associated with older age (≥ 45 years), male sex, initial choice of hemodialysis, diabetes mellitus, heart failure, coronary artery disease, cerebrovascular disease, and malignancy. In the patient group with SLE, independent predictors of mortality (p < 0.05) were older age (≥ 65 years), male sex, and diabetes mellitus. CONCLUSION: The longterm survival outcome was similar between patients with and without SLE who were on dialysis. The factors affecting patient mortality were not identical in these 2 groups.
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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 | 0.001 |
| 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".