Does cognitive impairment predict poor self-care in patients with chronic heart failure?
Bibliographic record
Abstract
Aims\nCognitive impairment occurs often in patients with chronic heart failure (CHF) and may contribute to sub-optimal self-care. This study aimed to test the impact of cognitive impairment on self-care. Methods and results\nIn 93 consecutive patients hospitalized with CHF, self-care (Self-Care of Heart Failure Index) was assessed. Multiple regression analysis was used to test a model of variables hypothesized to predict self-care maintenance, management, and confidence. Variables in the model were mild cognitive impairment (MCI; Mini-Mental State Exam and Montreal Cognitive Assessment), depressive symptoms (Cardiac Depression Scale), age, gender, social isolation, education level, new diagnosis, and co-morbid illnesses. Sixty-eight patients (75%) were coded as having MCI and had significantly lower self-care management (η2= 0.07, P < 0.01) and self-confidence scores (η2= 0.05, P < 0.05). In multivariate analysis, MCI, co-morbidity index, and NYHA class III or IV explained 20% of the variance in self-care management (P < 0.01); MCI made the largest contribution explaining 9% of the variance. Increasing age and symptoms of depression explained 13% of the variance in self-care confidence scores (P < 0.01). Conclusion\nCognitive impairment, a hidden co-morbidity, may impede patients' ability to make appropriate self-care decisions. Screening for MCI may alert health professionals to those at greater risk of failed self-care.
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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 | 0.006 |
| 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 | 0.000 |
| 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".