Care in the Home for the Management of Chronic Heart Failure
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to determine the effect of care in the home (CHM) compared with usual care (UC) in patients with chronic heart failure (CHF) on clinical outcomes and healthcare use including a cost-effectiveness analysis. METHODS: A systematic literature search on MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature, the Cochrane Library, as well as Centre for Reviews and Dissemination was conducted to identify randomized controlled trials comparing CHM with UC in CHF. The randomized controlled trials meeting inclusion criteria were meta-analyzed by outcome, and the quality of evidence for each outcome was evaluated using Grading of Recommendations Assessment, Development, and Evaluation system. A cost-effectiveness model was developed to estimate costs and quality-adjusted life years. RESULTS: Six randomized controlled trials were identified from 1277 citations. Care in the home was predominately provided by a single health professional consisting of nurse-led education of varying duration and frequency. One study included pharmacist-led CHM. Care in the home showed a decreased risk for all-cause mortality and hospitalizations combined (risk ratio, 0.88; 95% confidence interval [CI], 0.80-0.97), but not all-cause mortality alone (risk ratio, 0.92; 95% CI, 0.81-1.04). Care in the home resulted in fewer hospitalizations (mean difference, -1.03; 95% CI, -1.53 to -0.53) and fewer emergency department visits (mean difference, -1.32; 95% CI, -1.87 to -0.77). Quality of life also improved with CHM delivered by nurses. Critical appraisal of the quality of evidence suggests uncertainty in the estimates for a number of outcomes. Care in the home resulted in a savings of $10,665 and a gain of 0.11 quality-adjusted life years compared with UC. CONCLUSIONS: In conclusion, the beneficial effect of CHM in CHF is by reducing mortality and hospitalizations combined. Care in the home in CHF seems to be more effective and less costly compared with UC.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".