Early Cognitive Screening is recommended for the Population with Higher Blood Pressure Variation: A Cross‐sectional Study from a Blood Pressure Management Cohort in Hong Kong
Bibliographic record
Abstract
Abstract Background Cognitive impairment was shown to be associated with blood pressure (BP) and blood pressure variability (BPV), but the association with BPV is still uncertain in Chinese. This study explored whether BPV is associated with cognitive impairment among the elderly in Hong Kong. Method Participants in a community‐based cohort for regular BP measurements with health surveys were randomly invited to have a cognitive assessment with a shortened validated version of MoCA. Different thresholds of mild cognitive impairment (MCI) were used with reference to the age group and educational background. BPV was defined as standard deviation. K‐means clustering methods were applied to group the standard deviation of BPV into high, medium, and low variations. Logistic and quantile regression models were conducted to explore the association of MCI with systolic or diastolic BPV and a combined BPV classification. Odds ratios (OR) were adjusted for age, gender, educational background social economic status, and other medical histories, including hypertension, hyperlipidemia, diabetes, and stroke. Result A total of 573 participants with a mean age of 72 years were included. Most participants were females (86%). The median follow‐up times were eight months with a median number of nineteen BP records. Systolic BPV was shown to be associated with a higher risk of MCI (adjusted OR: 1.16; 95% CI: 1.03 to 1.31); whereas diastolic BPV was not significantly associated with MCI (adjusted OR: 1.05; 95% CI: 0.85 to 1.24) (Table 1). In the combined groupings for BPV classifications, participants with high BPV were shown to have around 5 times higher risk of MCI than those with low BPV. Conclusion Early stage of cognitive impairment was shown to be associated with systolic blood pressure variability in the Hong Kong population. Early cognitive screening for those with high blood pressure variability levels is recommended.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".