NEUROCOGNITIVE PRESERVATION LINKED TO ACE INHIBITOR THERAPY IN HEART FAILURE
Bibliographic record
Abstract
Cardio-selective medications, including angiotensin-converting enzyme (ACE) inhibitors—both centrally active and non-centrally active—and angiotensin receptor blockers (ARBs), have been investigated for cognitive benefits in heart failure (HF) patients. This secondary analysis of a cross-sectional study involved 90 community-dwelling adults with HF screened for cognitive impairment using the Montreal Cognitive Assessment (MoCA), with a mean score of 24.73 (SD = 2.76). T-tests revealed a significant association between ACE inhibitor use (78% of participants) and MoCA scores (p = 0.01), particularly for centrally active ACE inhibitors (60%), while no association was observed with ARB use. Bivariate analysis indicated that higher ACE inhibitor dosages correlated with improved cognitive function (r = 0.211, p = 0.046). In a multiple regression model adjusting for age, education, heart failure medication knowledge, and mean arterial blood pressure, ACE inhibitor use contributed an additional 4.1% to the explained variance in cognitive function and remained statistically significant (p = 0.021). Age and mean arterial blood pressure were not significant in the final model. These findings suggest a protective cognitive effect of ACE inhibitors in HF patients and support further investigation into their potential role in mitigating cognitive decline.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".