Angiotensin receptor blockers reduced the risk of post stroke cognitive impairment among first‐ever ischemic stroke patients
Bibliographic record
Abstract
Abstract Background The effect of antihypertensive drug classes in reducing the risk of post‐stroke cognitive impairment (PSCI) remains elusive. We aimed to investigate the association between antihypertensive drug classes and the risk of PSCI among ischemic stroke patients using data collected from a tertiary hospital in Indonesia. Methods We carried out a retrospective analysis of data from a medical database in Sardjito General Hospital Yogyakarta, Indonesia. We identified the first‐ever ischemic stroke patients hospitalized between June 2018 to May 2019 and whose serial measurements of cognitive function were available up to 90 days after stroke. The effect of antihypertensive drug classes on PSCI was measured by using multivariate logistic regression analysis. Results We found 71.11% patients developed PSCI at 90 days after stroke. Forty‐one patients (44.09%) received no antihypertensive drugs, 14 (15.05%) with angiotensin receptor blockers (ARB), 20 (21.50%) with calcium channel blockers (CCB), and 18 (19.35%) with combination of ARB and CCB. After controlling covariates, we found that ARB reduced the risk of PSCI by 64% (aOR 0.364, 95% CI = 0.14‐0.91, p<0.05), with improvement of total MoCA‐INA score by 3.113 points (p<0.05). Conclusions Among first‐ever ischemic stroke patients, ARB showed more favorable outcome on reducing the risk of PSCI and improving cognitive performance at 90 days after stroke. These findings may contribute to assist clinicians for selecting antihypertensive drug classes in PSCI prevention.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".