The Effect of Palliative Care Training on Symptom Management, Rehospitalization and Quality of Life in Chronic Heart Failure: A Randomized Controlled Trial
Bibliographic record
Abstract
Conclusion:The patients who received palliative care had a better symptom burden in the first month and a lower rehospitalization rate in the first, third, and sixth months. Palliative care should be integrated into the health care system to improve symptom management, increase the quality of life, and reduce rehospitalization among patients with heart failure. Trial Registration: clinicaltrials.gov Identifier: NCT05285163. Results:According to the Edmonton symptom assessment scale, tiredness (p=0.044), nausea (p=0.016), depression (p=0.002), anxiety (p=0.004), feeling of well-being (p=0.009), leg edema (p=0.021), and total symptom burden (p=0.027) in the first month after discharge and tiredness (p=0.042), nausea (p=0.014) and leg edema (p=0.042) in the third month after the discharge of intervention group was found to be significantly better than the control group. There was no significant difference between groups in quality of life. The rehospitalization rate at the first (p=0.001), third (p=0.001), and sixth (p=0.001) months in the intervention group was found to be significantly lower than the control group. Method:The study included 42 control and 42 intervention groups in patients with class III and IV heart failure according to New York Heart Association classification. Objective:Palliative care is of great importance because of the poor quality of life and high mortality risk in advanced heart failure. This study was planned as a randomized controlled trial to determine the effect of palliative care training on symptom management, rehospitalization, and quality of life among 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".