PREVALENCE OF COGNITIVE IMPAIRMENT IN A POPULATION WITH ATRIAL FIBRILLATION AND ITS RELATIONSHIP WITH ARTERIAL HYPERTENSION
Bibliographic record
Abstract
Objective: 1. Document the prevalence of mild cognitive impairment (MCI) in hypertensive patients with atrial fibrillation (AF). 2. Assess the association between blood pressure control and MCI in patients with AF. Design and method: A descriptive, cross-sectional study was conducted from June 2023 to February 2024 on patients treated at the Cardiology Department of Hospital Maciel. Patients included were those with atrial fibrillation (AF) receiving warfarin treatment who met the following criteria: completion of at least one monthly INR check in the past six months, maintenance of an adequate therapeutic anticoagulation time range (TTR), and provision of informed consent. Exclusion criteria included illiteracy, severe psychiatric disorders, mechanical mitral valves, alcoholism, or a history of stroke Data were collected from medical records using a pre-packaged questionnaire. The Montreal Cognitive Assessment (MoCa) was used as a screening test for MCI. A score of 24 or lower was defined as “altered”, following a study by Spósito P., & Llorens M. (2022) in patients treated at the Maciel Hospital. The hypertension variable was measured as the average of five consecutive ambulatory blood pressure measurements (AMPA). The TTR was calculated using the Rosendaal method. Results: The final sample comprised 131 patients with a mean age of 74.1 years and a median education level of 6 years. MCI was identified in 65% (85 patients). When comparing patients with altered MoCA scores to those with normal scores (>24), the altered group had a median systolic blood pressure (SBP) of 130 mmHg (range 120–140 mmHg), a mean age of 75.8 years, and a median education level of 6 years. The normal group showed a median SBP of 120 mmHg (range 120–130 mmHg), a mean age of 72.3 years, and a median education level of 10 years. These differences were statistically significant, Regarding AF patterns, a statistically significant association was observed between the paroxysmal pattern and MOCA greater than 24. Conclusions: The high prevalence of MCI highlights the importance of early screening in this population. The MCI group had fewer years of schooling, older age and worse SBP control.
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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 | 0.002 |
| 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 | 0.000 |
| 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".