Correlation between apathy and cognitive function in patients with cerebral small vessel disease
Bibliographic record
Abstract
Objective To explore the correlation between apathy and cognitive function in patients with cerebral small vessel disease (CSVD). Methods A total of 306 patients who were diagnosed with CSVD in Department of Neurology, the First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology from April 2023 to August 2024 were continuously included. According to the Modified Apathy Evaluation Scale (MAES) scores, the patients were divided into two groups: apathy (MAES>14 points, n=131) and non-apathy (MAES≤14 points, n=175). Their cognitive function was evaluated by Montreal Cognitive Assessment (MoCA) scale. Their clinical data and cognitive function scores were analyzed. The correlation between MAES scores and cognitire function in CSVD patients was evaluated. Results The apathy group showed increases in age, male ratio, and percentages of smoking history, diabetes, hypertension, and cerebrovascular disease history, compared with the non-apathy group (P<0.05). Their MoCA scores, including those for visuospatial and executive function, naming, attention, language, abstraction, delayed recall, orientation and years of education, were lower than the apathy group (P<0.05). The MAES score in CSVD patients was negatively correlated with MoCA score, visuospatial and executive function, naming, attention, language, abstraction, delayed recall, and orientation (all P<0.001). Furthermore, MoCA scores for visuospatial and executive function (OR=0.656, 95% CI: 0.464-0.926, P=0.017), attention (OR=0.609, 95% CI: 0.422-0.879, P=0.008), and delayed recall (OR=0.591, 95% CI: 0.433-0.806, P=0.001) were identified as independent risk factors for apathy in CSVD patients. Conclusions Compared with non-apathy patients, CSVD patients with apathy show significant differences in cognitive function. The more severe the cognitive impairment, the higher the degree of apathy.
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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 | 0.001 |
| 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".