Cognitive impairment in two subtypes of a single subcortical infarction
Bibliographic record
Abstract
BACKGROUND: Single subcortical infarction (SSI) is caused by two main etiological subtypes, which are branch atheromatous disease (BAD) and cerebral small vessel disease (CSVD)-related SSI. We applied the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ), the Shape Trail Test (STT), and the Stroop Color and Word Test (SCWT) to investigate the differences in cognitive performance between these two subtypes of SSI. METHODS: Patients with acute SSIs were prospectively enrolled. The differences of MoCA-BJ, STT, and SCWT between the BAD group and CSVD-related SSI group were analyzed. A generalized linear model was used to analyze the associations between SSI patients with different etiological mechanisms and cognitive function. We investigated the correlations between MoCA-BJ, STT, and SCWT using Spearman's correlation analysis and established cut-off scores for Shape Trail Test A (STT-A) and STT-B to identify cognitive impairment in patients with SSI. RESULTS: This study enrolled a total of 106 patients, including 49 and 57 patients with BAD and CSVD-related SSI, respectively. The BAD group performances were worse than those of the CSVD-related SSI group for STT-A (83 [60.5-120.0] vs. 68 [49.0-86.5], P = 0.01), STT-B (204 [151.5-294.5] vs. 153 [126.5-212.5], P = 0.015), and the number of correct answers on Stroop-C (46 [41-49] vs. 49 [45-50], P = 0.035). After adjusting for age, years of education, National Institutes of Health Stroke Scale and lesion location, the performance of SSI patients with different etiological mechanisms still differed significantly for STT-A and STT-B. CONCLUSIONS: BAD patients were more likely to perform worse than CSVD-related SSI patients in the domains of language, attention, executive function, and memory. The mechanism of cognitive impairment after BAD remains unclear.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".