Cognitive and self-care changes in patients with heart failure in the Amazon Region
Bibliographic record
Abstract
Heart failure is characterized as the lack of blood pumping capacity performed by the heart, which is considered a public health problem worldwide. Due to HF, the individual can develop clinical aspects that impact cognitive function and, consequently, self-care. Therefore, this study aimed to investigate cognitive changes and self-care in patients affected by HF and compare it with the cognitive and self-care changes of healthy participants. This is a quantitative, epidemiological, cross-sectional case-control study carried out at an institution in the city of Belém, Pará, Brazil. The following tests were used for data collection: Montreal Cognitive Assessment, Digit Symbol Substitution Test, European Heart Failure Self-care Behavior Scale (EHFScBS). Data were tabulated in Microsoft Excel 2010 and statistically treated by Epi Info version 3.5.2 with a 5% significance level and considering a 95% confidence interval in all analyzes. It was observed that patients with HF have slightly better self-care compared to patients without HF with scores obtained by EHFScBS equal to 29.7 ± 6.9 and 31.8 ± 8.2, respectively. Additionally, patients with HF showed impairments in the three cognitive domains, and women with HF demonstrated greater cognitive impairment compared to the other participants. The present study provides data to help build new approaches to interventions by the multidisciplinary team to promote better self-care and avoid cognitive impairments in patients with HF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".