Clinical–MRI Correlations in a Multiethnic Cohort with Recent Lacunar Stroke: The SPS3 Trial
Bibliographic record
Abstract
BACKGROUND: Neuroimaging manifestations of small vessel disease are heterogeneous, and correlation with patient features has not been adequately characterized. AIM: Our goal was to correlate magnetic resonance imaging findings with clinical features in a large multiethnic cohort with recent lacunar stroke. METHODS: Patient characteristics were correlated with neuroimaging results in the Secondary Prevention of Small Subcortical Stroke study participants. RESULTS: Among 3005 patients, mean age was 63 years; 62% were men; and 51%, 30%, and 16% were non-Hispanic White, Hispanic, and Black, respectively. Recent lacunar infarcts were distributed between the subcortical hemisphere (31%), thalamus (26%), brainstem/cerebellum (26%), and basal ganglia/internal capsule (16%). Multiple lacunar infarcts (i.e., acute and remote) were present in 40% and associated with increased age (OR 1·3 per 20 years, 95% CI 1·1, 1·5), male gender (OR 1·5, CI 1·3, 1·7), hypertension (OR 1·5, CI 1·2, 1·8), increased systolic blood pressure (OR 1·2 per 20 mmHg, CI 1·1, 1·3), and prior stroke (OR 3·8, CI 2·9, 5·0). Moderate-severe white matter hyperintensities were present in 50% and associated with increased age (OR 4·3 per 20 years, CI 3·4, 5·4), hypertension (OR 1·8, CI 1·4, 2·3), increased systolic blood pressure (OR 1·3 per 20 mmHg, CI 1·1, 1·5), increased diastolic blood pressure (OR 1·2 per 10 mm, CI 1·0, 1·3), and prior stroke (OR 3·3, CI 2·3, 4·5). Infarct location varied significantly by race-ethnicity (P < 0·001), with Blacks and Hispanics having more infarcts in the brainstem/cerebellum than non-Hispanic Whites, and by gender with women more often having thalamic lacunes than men (P ≤ 0·001). CONCLUSIONS: In patients with recent lacunar stroke, infarct location and number have distinctie associations with gender, vascular risk factors, and race-ethnicity, demonstrating the complex pathogenesis of lacunar stroke and cerebral small artery disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".