Abstract 156: Recurrent Stroke in Middle-Aged Lacunar Stroke Survivors: Understanding Risk Factors and Vulnerability in an Important Target Population
Bibliographic record
Abstract
Background: The precise role of stroke risk factors in middle-aged people remains a significant knowledge gap. Confounding risk factors conferred by young and elderly stroke are largely avoided, providing a unique opportunity to understand the relationship between traditional risk factors and stroke. We aimed to determine the predictors of stroke and myocardial infarction (MI) in a unique middle-aged cohort with MRI-defined lacunar stroke. Methods: We conducted a reanalysis of data from the Secondary Prevention of Small, Subcortical Strokes (SPS3) clinical trial. We focused on middle-aged study participants, defined as those aged 40-60 years at study entry. We used multivariate Cox regression models to estimate the risk of recurrent stroke or MI. Results: Out of 3,020 total subjects from SPS3, 1,312 (mean age 53 [SD 5], 445 females [34%]) were in the middle-aged category and were included in this analysis. Of these, there were 619 (47%) white, 297 (23%) black, and 283 (22%) Hispanic subjects. Over a mean follow-up time of 3.90 years, there were 123 strokes (2.6% per patient-year) and 33 MIs (0.7% per patient-year). The rates (% per patient-year) of having a stroke or MI differed significantly across white (n=59, 2.7%), black (n=54, 5.1%), and Hispanic (n=27, 2.8%) ethnic groups (p=0.006). Significant risk factors for recurrent stroke or MI are described for middle-aged and all SPS3 subjects in the table. Conclusions: In middle-aged individuals with prior stroke, diabetes, greater white matter disease severity, black race, and family history were potent predictors of recurrent stroke. Male sex, diabetes, and heart disease were potent predictors of MI. Compared to the entire SPS3 cohort, these predictors confer a greater risk for recurrent stroke or MI in the middle-aged. These data identify modifiable risk factors and target populations that are especially vulnerable to vascular events or complications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".