Improving Heart Failure Outcomes in Ambulatory and Community Care: A Scoping Study
Bibliographic record
Abstract
Despite a large body of literature testing interventions to improve heart failure care, care is often suboptimal. This scoping study assesses organizational interventions to improve heart failure outcomes in ambulatory settings. Fifty-two studies and systematic reviews assessing multicomponent, self-management support, and eHealth interventions were included. Studies dating from the 1990s demonstrated that multicomponent interventions could reduce hospitalizations, readmissions, mortality, and costs and improve quality of life. Self-management support appeared more effective when included in multicomponent interventions. The independent contribution of eHealth interventions remains unclear. No studies addressed management of comorbidities, geriatric syndromes, frailty, or end of life care. Few studies addressed risk stratification or vulnerable populations. Limited reporting about intervention components, implementation methods, and fidelity presents challenges in adapting this literature to scale interventions. The use of standardized reporting guidelines and study designs that produce more contextual evidence would better enable application of this work in health system redesign.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".