Individualizing the care of older heart failure patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: The heart failure epidemic is driven mainly by population aging and the improving survival of patients with cardiovascular risk factors. Aging heart failure patients are affected by multiple concurrent comorbidities and geriatric syndromes, the most important of which are frailty and cognitive impairment. The purpose of this review is to provide clinicians with practical advice on how to individualize the care of older heart failure patients. RECENT FINDINGS: Frailty and cognitive impairment are common in older heart failure patients. Frailty is increasingly recognized as a key risk factor for functional decline, health service utilization and mortality in aging heart failure patients. Similarly, cognitive impairment impairs patients' ability for self-care and leads to adverse outcomes. Simple and efficient instruments exist to screen for these conditions. Heart failure patients who are frail or cognitively impaired are best looked after in a disease management setting that is deployed in a more integrated healthcare system with access to specialized geriatric consultants. Optimal care planning requires knowledge of these conditions as well as patient and caregiver engagement. SUMMARY: Frailty and cognitive impairment are central features of the heart failure syndrome in aging patients and should be routinely considered in assessment and care planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".