Risks, Subtypes, and Hospitalization Costs of Stroke Among Patients with Systemic Lupus Erythematosus: A Retrospective Cohort Study in Taiwan
Bibliographic record
Abstract
OBJECTIVE: To compare risks, subtypes, and hospitalization costs of stroke between cohorts with and without systemic lupus erythematosus (SLE). METHODS: From the catastrophic illnesses registry of Taiwan's universal health insurance claims data, we identified 13,689 patients with SLE diagnosed in 1997-2008 and selected 54,756 non-SLE controls, frequency-matched with age (every 5 years), sex, and index year. Age-specific and type-specific stroke incidence, hazard, and cost of stroke were compared between the 2 cohorts to the end of 2008. RESULTS: Compared with the non-SLE cohort, the risk of stroke was 3.2-fold higher in the SLE cohort (5.53 vs 1.74 per 1000 person-years) with an overall adjusted HR of 2.90 (95% CI 2.52-3.33). The age-specific risk was the highest in patients 1-17 years old (HR 163, 95% CI 22.2-1197) and decreased as age increased (p = 0.004). Hypertension and renal disease were the most important comorbidities in the SLE cohort predicting stroke risk (HR 1.75, 95% CI 1.28-2.39 and HR 1.66, 95% CI 1.32-2.10, respectively). There were more hemorrhagic strokes in the SLE cohort than in the non-SLE cohort, but not significantly (28.0% vs 23.4%; p = 0.10). The hospitalization cost for stroke patients was more than twice the cost for those with SLE than for those without (p < 0.0001). CONCLUSION: Stroke risk and hospital care costs are considerably greater for patients with SLE than without. The relative risk of stroke is the highest in young 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".