Highly Variable Blood Pressure as a Predictor of Poor Cognitive Outcome in Patients With Acute Lacunar Infarction
Bibliographic record
Abstract
OBJECTIVE AND BACKGROUND: Many patients develop cognitive impairment after an acute stroke. It is not clear whether blood pressure variability is a prognostic factor for cognitive impairment. We aimed to determine the association between blood pressure variability on hospital admission and cognitive outcome in patients with acute lacunar infarction. METHODS: We performed a retrospective analysis on 22 men and 14 women (mean age, 61.8 years) who had completed a cognitive evaluation 3 months after onset of an acute lacunar infarction. The patients had no previous functional disability or dementia, stenosis in major cerebral arteries, cardiac embolic sources, or infarct in strategic territories for cognition. We used standard deviation and coefficient of variance as parameters of blood pressure variability, and each cognitive function test z score as an outcome parameter. We performed linear regression analysis to assess the relationship between blood pressure variability and cognition, adjusted for vascular risk factors, severity of neurologic deficits, and mean blood pressure. RESULTS: High variability of both systolic and diastolic blood pressure was significantly associated with low z scores on the Controlled Oral Word Association Test and the Digit Symbol Coding test (P<0.01). High variability of diastolic blood pressure was significantly associated with low z scores on the Korean Mini-Mental State Examination and Seoul Verbal Learning Test delayed recall (P<0.01). CONCLUSIONS: Highly variable blood pressure on admission for acute lacunar infarction may predict poor cognitive outcomes, especially frontal lobe dysfunction.
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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 | 0.004 |
| 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".