Protective effects of beta-blockers in cerebrovascular disease
Bibliographic record
Abstract
OBJECTIVE: Because activated sympathetic tone is associated with poorer outcome after stroke, we investigated whether beta-blocker treatment was associated with lesser stroke severity and improved outcome. METHOD: We prospectively studied 111 patients with stroke. Stroke severity on presentation gauged by Canadian Neurologic Scale (CanNS) and medication use verified from medical records. Power spectral analysis of heart rate variability estimated cardiac sympathovagal tone. Coagulation and inflammatory activity were assessed. RESULTS: On multiple linear regression, beta-blocker use was the sole independent predictor of less severe stroke on presentation (95% CI: 0.12 to 1.86: p = 0.03). When CanNS was dichotomized, multiple logistic regression revealed that beta-blocker use (odds ratio [OR] 3.70, 95% CI: 1.24 to 11.01, p = 0.02) and female gender (OR 2.96, 95% CI: 1.14 to 7.69, p = 0.03) were independent predictors of CanNS score >8.5. There was no difference in blood pressure and blood glucose between these two groups. Beta-blocker treatment was associated with lower sympathovagal tone (p = 0.001), thrombin (p = 0.009), hemoglobin A(1)C levels (p = 0.02), and erythrocyte sedimentation rate (p = 0.003). CONCLUSION: Beta-blocker use is associated with less severe stroke on presentation and may be cerebroprotective due to a sympatholytic effect associated with decreased thrombin, inflammation, and hemoglobin A(1)C.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".