Increased Risk of Ischemic Stroke in Systemic Sclerosis: A National Cohort Study of US Veterans
Bibliographic record
Abstract
OBJECTIVE: Previously thought to involve primarily the microvasculature, systemic sclerosis (SSc) has been increasingly linked to macrovascular disease. Cardiovascular (CV) and cerebrovascular disease are responsible for 20-30% of mortality in SSc, but few studies have shown an independent association between SSc and stroke. We assessed whether SSc was an independent risk factor for ischemic stroke. METHODS: We conducted a retrospective cohort study using the national Veterans Affairs (VA) administrative database containing records from 1999 to 2014. We obtained data for all patients with a diagnosis of SSc as well as 2 controls per SSc patient matched on sex, race, smoking status, and VA site. All patients were followed until development of ischemic stroke, death, or last encounter. We used a Cox proportional hazard regression model to estimate risk of ischemic stroke, with adjustments for CV comorbidities (hypertension, diabetes, atrial fibrillation, non-cerebrovascular atherosclerotic disease, hyperlipidemia), baseline medication use (aspirin, nonsteroidal antiinflammatory drugs), and Medicare enrollment. RESULTS: Among 4545 individuals with SSc (83% male, mean age 60.9 yrs), the incidence rate of ischemic stroke was 15.3 per 1000 person-years (vs 12.2 in the control cohort), with an unadjusted HR 1.28 (95% CI 1.11-1.47). The adjusted HR was 1.21 (95% CI 1.05-1.40) after adjusting for baseline CV risk factors, medications, and Medicare enrollment. CONCLUSION: SSc is independently associated with a higher risk of ischemic stroke among US veterans. Patients with SSc represent a population likely to benefit from targeted stroke screening or prevention therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".