Effect of Palliative Care for Patients with Heart Failure
Bibliographic record
Abstract
Palliative care might be beneficial to heart failure. However, the results remain controversial. We conducted a systematic review and meta-analysis to explore the effect of palliative care on heart failure.PubMed, Embase, Web of Science, EBSCO, and Cochrane library databases were systematically searched. Randomized controlled trials (RCTs) assessing the effect of palliative care versus usual care on heart failure were included. Two investigators independently searched articles, extracted data, and assessed the quality of included studies. The primary outcome was readmission. Meta-analysis was performed using random-effect model.Five RCTs involving 545 patients were included in the meta-analysis. Overall, compared with control intervention, palliative care intervention was found to significantly reduce the readmission [Std. mean difference = 0.79; 95% confidence intervals (CI) = 0.23 to 1.35; P = 0.006], Edmonton Symptom Assessment Scale (ESAS) (Std. mean difference = -2.5; 95% CI = -4.39 to -0.62; P = 0.009), and PHQ-9 (Std. mean difference = -1.16; 95% CI = -1.73 to -0.58; P < 0.005), as well as improve heart failure questionnaire (Std. mean difference = 4.46; 95% CI = 3.44 to 5.47; P < 0.005), but had no influence on mortality (RR = 1.54; 95% CI = 0.80 to 2.96; P = 0.19) and quality of life questionnaire (Std. mean difference = 1.81; 95% CI = -0.14 to 3.77; P = 0.07).Compared with control intervention, palliative care intervention was found to significantly reduce readmission, ESAS, PHQ-9, and improve heart failure questionnaire, but showed no influence on mortality and quality of life questionnaire in patients with heart failure.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.020 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".