Montreal Cognitive Assessment of cognitive dysfunction after basal ganglia stroke
Bibliographic record
Abstract
OBJECTIVE: The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive dysfunction after basal ganglia stroke, and factors affecting total MoCA score were examined. METHODS: Data were retrospectively analyzed for 30 patients with basal ganglia intracerebral hemorrhage or basal ganglia cerebral infarction, who were admitted to The Second Affiliated Hospital of Fujian Traditional Medical University (Fujian, China) from January 2017 to March 2020. Cognitive impairment was assessed using the MoCA, and potential correlations were explored between clinicodemographic characteristics (sex, age, stroke location and etiology) and MoCA dimensions or total MoCA score. RESULTS: Univariate linear regression showed that the total MoCA score was significantly associated with sex, age, executive function, naming, attention, abstract generalization ability, memory ability, and visuospatial orientation. However, multivariate linear regression identified only executive function, naming, attention, memory ability, and visuospatial orientation as significantly associated with the total MoCA score. CONCLUSIONS: We showed that the MoCA test can be used for patients with basal ganglia stroke. The total MoCA score of basal ganglia stroke was significantly associated with impairments in executive function, naming, attention, memory ability, and visuospatial orientation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".