Beta-blockers in elderly patients: neuroprotective effect or risk of cognitive decline?
Bibliographic record
Abstract
BACKGROUND: Due to the fact that the number of elderly people with cognitive disorders is steadily increasing worldwide, there is an increased interest in studying the effects of drugs of different pharmacological groups on cognitive function. For many years, beta-blockers have been one of the main groups in the therapy of cardiovascular diseases. The effect of beta-blockers on cognitive function has been studied for a long time, and there is different, sometimes contradictory data on this issue. AIM: To evaluate the incidence of cognitive impairment in elderly and to determine the associations between cognitive impairment and the beta-blockers use. MATERIALS AND METHODS: Cross-sectional study included all patients aged 60 years and older who attended the ambulance care from 24.10.2019 to 15.12.2019 at the polyclinic No. 78 in Saint Petersburg. Measurements: the Montreal cognitive assessment (MoCA) test, the 15-item Geriatric Depression Scale. Data collection included a full medical history, a medication review and questionnaire. RESULTS: The prevalence of cognitive impairment among the study participants was 71.1% (n = 138). Сognitive impairment was associated with high blood pressure and a history of stroke (p 0.05). Beta-blockers use was associated with decreased in total MoCA score, fluency (p = 0.0115), thinking (p = 0.0012), and memory (p = 0.0040). The identified association remained statistically significant after adjusting for gender, age, high blood pressure, a history of stroke, level of education, and decreased emotional background with odds ratio 2.245 (95% confidence interval 1.1564.358) for the fluency test and coefficient of regression 0.781 (95% confidence interval [1,233][0,328]) for delayed memory. CONCLUSIONS: Memory impairment (coefficient of regression 0.781, 95% confidence interval 1.233 to 0.328) and decreased fluency (odds ratio 2.245; 95% confidence interval 1.1564.358) were observed in the study in the outpatient elderly patient population taking beta-blockers. The beta-blockers may lead to memory impairment. When choosing hypotensive therapy, all possible effects of beta-blockers should be considered, including the effect on cognitive status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".