Complementary Musical Intervention for Patients in Palliative Care in Spain: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Patients with advanced cancer often endure a heavy burden of symptoms, both in quantity and intensity. Complementary therapies offer potential relief in this challenging scenario. Increasing the number of randomized controlled trials provides a unique opportunity to generate rigorous data, which can be used to establish causal relationships and evaluate interventions; hence, nurses can strengthen evidence-based practices, leading to better patient outcomes and quality of care. Our study aimed to evaluate the impact of a 7-day pre-recorded music intervention on cancer symptoms and satisfaction in advanced-stage cancer patients receiving palliative care at home. Methods: This multicenter, double-blind, randomized, controlled clinical trial involved 80 Spanish cancer patients receiving palliative care at home, and was conducted from July 2020 to November 2021. The intervention group (n = 40) received self-selected pre-recorded music for 30 min daily over 7 days. The control group (n = 40) received pre-recorded basic health education sessions of equal duration and frequency. Symptoms and patient satisfaction were assessed before and after the intervention using the Edmonton Symptom Assessment System and the Client Satisfaction Questionnaire, respectively. Results: Comparing the intervention with the control group, significant improvements were observed in various symptoms: total symptom burden (p < 0.001), pain (p = 0.001), fatigue (p = 0.007), depression (p = 0.001), anxiety (p = 0.005), drowsiness (p = 0.006), appetite (p = 0.047), well-being (p ≤ 0.001), and sleep (p < 0.001); additionally, patient satisfaction was higher in the intervention group (p < 0.001). Conclusions: The 7-day pre-recorded music intervention reduced both physical and psychological symptoms in advanced-stage cancer patients receiving home-based palliative care, demonstrating significant alleviation of overall symptom burden and increased satisfaction with healthcare.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".