Infusion of Sound: Personalized Receptive Music-Based Intervention (rMBI) During Infusion Sessions
Bibliographic record
Abstract
Background: Nonpharmacological methods to manage symptoms of cancer and its treatment sequelae, including receptive music-based interventions (rMBIs), have gained traction due to their limited adverse effects and beneficial impact. Despite these benefits, implementation and analysis of personalized rMBIs in the infusion setting remain limited. Objectives: This study provided patients with a personalized rMBI during their infusion session and assessed changes in symptom burden and vitals to evaluate intervention efficacy. Design: The rMBI involved listening to a personalized playlist on Spotify on an iPad for 30 minutes and was assessed both qualitatively and quantitatively. Outcomes include vitals and symptom burden (measured with the Edmonton Symptom Assessment Scale, ESAS) pre- and post-rMBI; changes were analyzed with paired t -tests. Post-rMBI, patient reflections were collected and analyzed with a rapid qualitative analysis approach. Settings/Subjects: This is a self-controlled case series among adult patients receiving infusion therapy at a single academic community hospital in the United States. Measurements/Results: A total of 50 participants were recruited. From the ESAS, rMBI led to significant decreases in pain ( p = 0.011), tiredness ( p < 0.0001), nausea ( p = 0.014), anxiety ( p = 0.005), and shortness of breath ( p = 0.002), as well as a significant increase in feelings of well-being ( p < 0.0001). Heart rate ( p < 0.0001) and systolic blood pressure ( p = 0.0234) also decreased post-rMBI. Patient narratives demonstrated common themes of escape, reflection/nostalgia, comfort/peace, hope, and rejuvenation. Conclusions: Personalized rMBIs are effective in managing symptoms and enhancing overall well-being in patients receiving infusions. These results support incorporating rMBIs as part of the patient experience and standard of care at infusion centers (NCT06450626).
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".