Vascular Risk Factors and Cognition in Multiple System Atrophy
Bibliographic record
Abstract
Objective: Vascular risk factors have been reported to be associated with cognitive impairment (CI) in the general population, but their role on CI in multiple system atrophy (MSA) is unclear. This study aimed to explore the relationship between vascular risk factors and CI in patients with MSA. Methods: The clinical data and vascular risk factors were collected. The Montreal Cognitive Assessment tool was used to test the cognitive function of patients with MSA. Binary logistic regression was used to analyze the correlation between vascular risk factors and CI. Results: A total of 658 patients with MSA with a mean disease duration of 2.55 ± 1.47 years were enrolled. In MSA patients, hypertension was recorded in 20.2%, diabetes mellitus in 10.3%, hyperlipidemia in 10.2%, smoking in 41.2%, drinking in 34.8%, and obesity in 9.6%. The prevalence of CI in patients with MSA, MSA with predominant parkinsonism (MSA-P), and MSA with predominant cerebellar ataxia (MSA-C) was 45.0, 45.1, and 44.9%, respectively. In the binary logistic regression model, patients with more than one vascular risk factors were significantly more likely to have CI in MSA (OR = 4.298, 95% CI 1.456–12.691, P = 0.008) and MSA-P (OR = 6.952, 95% CI 1.390–34.774, P = 0.018), after adjusting for age, sex, educational years, disease duration, and total Unified multiple system atrophy rating scale scores. Conclusion: Multiple vascular risk factors had a cumulative impact on CI in MSA. Therefore, the comprehensive management of vascular risk factors in MSA should not be neglected.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".