Associations between Autonomic Function and Cognitive Performance among Patients with Cerebral Small Vessel Disease
Bibliographic record
Abstract
Data linking heart rate variability (HRV) and cognitive status remains controversial and scarce, particularly in cerebral small vessel disease (CSVD) patients. Whether the association between HRV and cognitive performance exists in CSVD patients is unclear. Hence, we aimed to investigate the association between HRV and cognitive performance in patients with CSVD. This cross-sectional study was conducted among 117 CSVD patients. All patients underwent HRV assessment and global cognitive evaluation by the Mini-Mental-State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Multivariable analyses were performed to evaluate the association between HRV and cognitive status. The mean age of study population was 59.5 ± 11.8 years and 39.3% were female. After adjusting for confounding factors, a higher high frequency (HF) norm was independently associated with better MMSE scores (β = 0.051; 95% confidence interval (CI): 0.012~0.090; p = 0.011) and MoCA scores (β = 0.061; 95% CI: 0.017~0.105; p = 0.007), while a higher low frequency (LF)/HF ratio was independently associated with worse MMSE scores (β = −0.492; 95% CI: −0.893~−0.092; p = 0.017) and MoCA scores (β = −0.691; 95% CI: −1.134~−0.248; p = 0.003). The HF norm was positively associated with global cognitive performance, whereas the LF/HF ratio was negatively associated with global cognitive performance among CSVD patients. Further study of the relationship between autonomic function and cognitive performance is warranted.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".