Inpatient Music Therapy Effects On Self-Reported Symptoms In A Cancer Center : A Preliminary Report
Bibliographic record
Abstract
Introduction:Music therapy has shown benefits for reducing distress in individuals with cancer. We explore the effects of music therapy on self-reported symptoms of patients receiving inpatient care at a comprehensive cancer center. Methods:Music therapy was available as part of an inpatient integrative oncology consultation service; we examined interventions and symptoms for consecutive patients treated by a board certified music therapist from 9/2016-5/2017. Patients completed the Edmonton Symptom Assessment Scale (ESAS, 10 symptoms, scale 0-10, 10 most severe) before and after the intervention. Data was summarized by descriptive statistics. Change in ESAS symptom and subscale scores [physical distress (PHS), psychological distress (PSS), and global distress (GDS)] were evaluated by Wilcoxon signed rank test.Results:Data were evaluable for 96 of 100 consecutive patients; 55% were women, average age 50, majority with hematologic malignancies (47%). Reasons for music therapy referral included: anxiety/stress (67%), adjustment disorder/coping (28%), and mood elevation/depression (17%). Highest (worst) symptoms at baseline were sleep disturbance (5.7) and well-being (5.5). We observed statistically and clinically significant improvement (means) for anxiety (-2.3u00b11.5), drowsiness (-2.1u00b12.2), depression (-2.1u00b11.9), nausea (-2.0u00b12.4), fatigue (-1.9u00b11.5), pain (-1.8u00b11.4), shortness of breath (-1.4u00b12.2), appetite (-1.1u00b11.7); and for all ESAS subscales (all pu2019s<0.02). Highest clinical response rates were observed for anxiety (92%), depression (91%), and pain (89%).Conclusions:A single, live, tailored music therapy intervention as part of an integrative oncology inpatient consultation service contributed to significant improvement in global, physical and psychosocial distress. A randomized controlled trial is justified.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".