Clinical Correlates of Infarct Shape and Volume in Lacunar Strokes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Infarct size and location are thought to correlate with different mechanisms of lacunar infarcts. We examined the relationship between the size and shape of lacunar infarcts and vascular risk factors and outcomes. METHODS: We studied 1679 participants in the Secondary Prevention of Small Subcortical Stroke trial with a lacunar infarct visualized on diffusion-weighted imaging. Infarct volume was measured planimetrically, and shape was classified based on visual analysis after 3-dimensional reconstruction of axial MRI slices. RESULTS: Infarct shape was ovoid/spheroid in 63%, slab in 12%, stick in 7%, and multicomponent in 17%. Median infarct volume was smallest in ovoid/spheroid relative to other shapes: 0.46, 0.65, 0.54, and 0.90 mL, respectively (P<0.001). Distributions of vascular risk factors were similar across the 4 groups except that patients in the ovoid/spheroid and stick groups were more often diabetic and those with multicomponent had significantly higher blood pressure at study entry. Intracranial stenosis did not differ among groups (P=0.2). Infarct volume was not associated with vascular risk factors. Increased volume was associated with worse functional status at baseline and 3 months. Overall, 162 recurrent strokes occurred during an average of 3.4 years of follow-up with no difference in recurrent ischemic stroke rate by shape or volume. CONCLUSIONS: In patients with recent lacunar stroke, vascular risk factor profile was similar among the different infarct shapes and sizes. Infarct size correlated with worse short-term functional outcome. Neither shape nor volume was predictive of stroke recurrence. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT00059306.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".