Multiple Hypointense Vessels are Associated with Cognitive Impairment in Patients with Single Subcortical Infarction
Bibliographic record
Abstract
We aimed to explore the relationship between multiple hypointense vessels and cognitive function in patients with single subcortical infarction (SSI) and the role of SSI with different etiological mechanisms in the above relationship. Multiple hypointense vessels were measured by the number of deep medullary veins (DMVs), DMVs score, and cortical veins (CVs) score. The Montreal Cognitive Assessment (MoCA), the Shape Trail Test (STT), and the Stroop Color and Word Test (SCWT) were assessed to evaluate cognitive function. SSI was dichotomized as branch atheromatous disease (BAD) and cerebral small vessel disease (CSVD)-related SSI by whole-brain vessel-wall magnetic resonance imaging. We included a total of 103 acute SSI patients. After adjustments were made for related risk factors of cognitive function, the SSI patients with higher DMVs score were more likely to have longer STT-B (P = 0.001) and smaller STT-B-1 min (P = 0.014), and the SSI patients with higher CVs score were more likely to have shorter STT-A (P = 0.049). In subgroup analysis, we found that the negative relationship between DMVs scores and cognitive function and the positive relationship between CVs scores and cognitive function were significantly stronger in BAD patients. We provided valuable insights into the associations between DMVs, CVs, and multi-domain cognitive impairment in SSI patients, which underscored the necessity to further study the dynamic alterations of venules and their specific influence on post-stroke cognitive impairment.
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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) | 0.001 | 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 | 0.001 |
| 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".