Effectiveness of Palliative Care in Advanced Heart Failure: A Comprehensive Meta-Analysis
Bibliographic record
Abstract
Advanced heart failure (HF) significantly diminishes quality of life (QoL) due to severe symptom burden and psychological distress. This meta-analysis investigates the impact of palliative care interventions on QoL, psychological well-being, symptom burden, and mortality in adults with advanced HF. A systematic search of major databases through August 2024 identified 13 randomized controlled trials involving 1541 participants from the USA, China, Iran, and Sweden. Palliative care significantly improved QoL scores compared with usual care [mean difference (MD) = 1.36; 95% confidence interval (CI), 0.89-1.83; P < 0.00001], with notable improvements using the Kansas City Cardiomyopathy Questionnaire. Palliative care significantly reduced anxiety scores (MD = -0.39; 95% CI, -0.66 to -0.11), P = 0.006), especially when assessed with the Edmonton Symptom Assessment Scale. While no significant differences were observed for dyspnea or depression (P =0.56, P = 0.23, respectively). Mortality rates did not differ significantly between groups (P = 0.61). These findings highlight the potential of palliative care, particularly interventions incorporating symptom management and psychological support, to enhance QoL and reduce anxiety in advanced HF patients. However, further research is required to standardize these interventions and evaluate their long-term outcomes.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".