Impact of Massage Therapy on the Quality of Life of Hospice Patients and Their Caregivers: A Pilot Study
Bibliographic record
Abstract
Evidence for massage therapy (MT) in hospice patients remains limited. We conducted a prospective pilot study on MTs impact on quality of life of hospice patients and caregivers. Patient-caregiver dyads were enrolled if patients scored ≥5 on pain, depression, anxiety, or well-being using the revised Edmonton Symptom Assessment System Revised (ESAS-r). The patient received MT weekly for up to 3 massages with assessments completed at baseline, after each massage, and 1 week after the final massage for patients and at baseline and 1 week after final massage for caregivers. A satisfaction survey was completed at study completion. A pro-rated area under the curve (AUC) was utilized to assess the primary endpoints of change in ESAS-r for patient ratings of pain, depression and anxiety as well as the Linear Analogue Self-Assessment (LASA). Median difference scores (end of study value)-(baseline value) for each participant and caregiver were calculated. Of 27 patients and caregivers enrolled, 25 patients received MT. Fifteen patients completed all 3 MT sessions and were given the final symptom assessment and satisfaction survey and their caregivers completed final assessments. The proportion of patients considered success (AUC > baseline) in the primary endpoints were the following: pain 40.9%, depression 40.9%, anxiety 54.5%, LASA 54.5%. Median difference scores were largely zero indicating no significant temporal change in symptoms. Patients were highly satisfied with MT. This pilot study indicated that MT was a feasible and well-received intervention in our population of patients with inadequately controlled symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".