Hemispheric Asymmetry of White Matter Hyperintensity in Association With Lacunar Infarction
Bibliographic record
Abstract
Background White matter hyperintensities ( WMHs ) are commonly asymmetric between hemispheres but for unknown reasons. We investigated asymmetric WMHs associated with lacunar infarcts. Methods and Results A total of 267 consecutive patients with small first-ever supratentorial infarcts (≤20 mm) were included. None had a relevant vascular stenosis. WMH asymmetry was measured based on the hemispheric difference of a modified Scheltens scale score (≥3 defined as asymmetric). We analyzed the association of the hemispheric WMH asymmetry with old silent lacunar infarcts or acute lacunar infarcts. We compared lesion frequency maps between groups and generated t-statistics maps. The mean age of patients was 64 years, and 63% were men. Asymmetric WMH was more than 3-fold as frequent ( P<0.001) in the group with old silent lacunar infarcts (42%, 43/102) than in the group without old silent lacunar infarcts (15%, 24/165). In patients with left hemispheric dominance of WMHs , an acute lacunar infarct was more likely to be located in the left (versus right) hemisphere (74% versus 26%, P<0.001). In patients with right hemispheric dominance of WMHs , an acute lacunar infarct was more likely to be located on the right (versus left) hemisphere (81% versus 19%, P<0.001). Mapping studies showed that the side of hemispheric dominance of WMHs was associated with acute and silent lacunes on the same side. Conclusions These are the first data to show that asymmetric WMH s are associated with both old silent lacunar infarcts and acute lacunar infarcts ipsilateral to the greatest WMH burden. This suggests that the hemisphere with relatively large WMHs is more vulnerable to ischemia.
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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 | 0.001 | 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) | 0.002 | 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".