Nonhypertensive White Matter Hyperintensities in Stroke: Risk Factors, Neuroimaging Characteristics, and Prognosis
Bibliographic record
Abstract
Background This study explored the risk factors, neuroimaging features, and prognostic implications of nonhypertensive white matter hyperintensity (WMH) in patients with acute ischemic stroke and transient ischemic attack. Methods and Results We included 2283 patients with hypertension and 1003 without from a pool of 10 602. Associations of moderate‐to‐severe WMH with known risk factors, functional outcome, and a composite of recurrent stroke, myocardial infarction, and all‐cause mortality were evaluated. A subset of 351 patients without hypertension and age‐ and sex‐matched pairs with hypertension and moderate‐to‐severe WMH was created for a detailed topographic examination of WMH, lacunes, and microbleeds. Approximately 35% of patients without hypertension and 65% of patients with hypertensive stroke exhibited moderate‐to‐severe WMH. WMH was associated with age, female sex, and previous stroke, irrespective of hypertension. In patients without hypertension, WMH was associated with initial systolic blood pressure and was more common in the anterior temporal region. In patients with hypertension, WMH was associated with small vessel occlusion as a stroke mechanism and was more frequent in the periventricular region near the posterior horn of the lateral ventricle. The higher prevalence of occipital microbleeds in patients without hypertension and deep subcortical lacunes in patients with hypertension were also observed. Associations of moderate‐to‐severe WMH with 3‐month functional outcome and 1‐year cumulative incidence of the composite outcome were significant (both P <0.01), although the latter lost significance after adjustments. The associations between WMH and outcomes were consistent across hypertensive status. Conclusions One‐third of patients without hypertension with stroke have moderate‐to‐severe WMH. The pathogenesis of WMH may differ between patients without and with hypertension, but its impact on outcome appears similar.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".