Cluster Analysis of Different Impaired Cognitive Domains in Patients With Post-Stroke Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVES: A cluster analysis was conducted to classify the 7 different cognitive domains affected by PSCI patients, to explore the correlation and similarity between cognitive domains and provide a basis for targeted intervention. METHODS: We collected demographic and disease-related data from 724 PSCI patients. We used Python 3.8 software to perform K-means clustering and hierarchical clustering on the 7 cognitive domains assessed by the MoCA scale, and used the silhouette coefficient to determine the optimal number of clusters k. RESULTS: The results of K-means clustering and hierarchical clustering show that the 7 dimensions of MoCA can be grouped into 2 categories. Cluster 1 scored lower in the cognitive areas of visual space and executive function, attention, language, abstraction, and delayed recall, whereas cluster 2 had higher scores in the naming and orientation domains. The scores in all cognitive domains of cluster 1 are lower than those of cluster 2, indicating severe cognitive impairment. Compared with cluster 2, the subjects in cluster 1 have poor physical health, living conditions, economic status, and social support ability. CONCLUSIONS: The 7 dimensions of MoCA can be divided into 2 categories. In clinical practice, health care professionals should pay special attention to the severity of the patient's condition, the affected area, and individual differences, and develop precise and personalized treatment plans to improve the patient's cognitive function and quality of life.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".