MORNING BLOOD PRESSURE SURGE PREDICTS PERFORMANCE IN TASK-SWITCHING AND PROCESSING SPEED IN THE ELDERLY
Bibliographic record
Abstract
While higher morning systolic blood pressure surges (MBPS) have been associated to increased stroke risk, the association with cognitive performances remains unknown. The aim of this study is to determine which method for calculating MBPS is the best predictor of performance in task-switching and processing speed in both elderly normotensive and hypertensive subjects. One hundred and three participants between 60–75 years old were divided into three groups: normotensive subjects not receiving an anti-hypertensive treatment (n=49), hypertensive subjects receiving treatment and controlled for BP (n=28) and refractory hypertensive subjects (n=26). Subjects were evaluated for ambulatory blood pressure (BP) and cognitive functions using a battery of neuropsychological tests. Four methods for calculating MBPS (pre-waking surge, morning-evening surge, rising BP surge and sleep through surge) were selected and used individually as independent determinants in multiple-linear-regression models together with group, age, sex, years of schooling while using preselected cognitive variables as outcomes. Models using pre-waking surge (Morning BP minus Pre-awake BP) were significant predictors of “the number of errors in the Trial-Making-Test Part B(TMTB)”(p=0.018), “the number of switching errors in TMTB”(p=0.005), and “the reading condition of the Color-Word Interference Test”(p=0.031), while models using morning-evening surge (Morning BP minus Evening BP) were significant predictors of “the number of errors in TMTB”(p=0.036), “the number of switching errors in TMTB”(p=0.02). The other methods of calculating MBPS were less successful predictors. These results suggest that pre-waking surge could be used as a predictor of performance in task-switching and processing speed in elderly subjects independently of their BP-related category.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".