Abstract P699: Trends in Ischemic Stroke Risk Factors and Outcomes in a Rural Population in the United States
Bibliographic record
Abstract
Introduction: The stroke mortality rate has gradually declined due to improved interventions and controlled risk factors. We investigated the trends in stroke risk factors and outcomes among a rural population in the United States between 2004 and 2018. Methods: We built a comprehensive stroke database called “Geisinger NeuroScience Ischemic Stroke (GNSIS)” for this study. Clinical data were extracted from multiple sources, including electronic health records and quality data. Results: Our cohort comprised of 8,561 consecutive ischemic stroke patients (mean age: 70.1±13.9 years, men: 51.6%, 95.1% Caucasian). Hypertension was the most prevalent risk factor (75.2%). The rate of hypertension, diabetes, dyslipidemia, and history of stroke increased significantly over the fifteen years window. The one-year recurrence and mortality rates were 6.3% and 15.8%, respectively. Although the one-year stroke recurrence increased from 2004 to 2018 (Cochran-Armitage test Z = -3.66, p<0.001), the one-year stroke mortality rate decreased significantly (Cochran-Armitage test Z = 2.39, p=0.008). Age >65 years, atrial fibrillation or flutter, heart failure, and prior ischemic stroke were independently associated with one-year all-cause mortality in stratified Cox proportional hazards model. In the Fine-Gray competing risk model, diabetes mellitus and age <65 years was found to be associated with one-year ischemic stroke recurrence. In the logistic regression, chronic kidney disease (CKD), diabetes, and prior ischemic stroke were predictors of one-year recurrence while age >65 years, atrial fibrillation or flutter, CKD, heart failure, prior hemorrhagic and ischemic stroke, history of neoplasm, myocardial infarction, and rheumatic diseases were predictors of one-year mortality. Conclusion: Although stroke mortality has decreased, stroke recurrence and several vascular risk factors have significantly increased in our rural population between 2004-2018. Older age, atrial fibrillation or flutter, heart failure, and prior ischemic stroke were independently associated with one-year all-cause mortality while diabetes mellitus and age less than 65 years were predictors of ischemic stroke recurrence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".