Massage Therapy Utilization in Pediatric Acute Burns: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Patient-centered burn care extends beyond physical treatment to incorporate the management of the psychological impacts including stress, pain, and anxiety. This study explores the novel application of massage therapy (MT) in children with acute burns, assessing utilization and impact on pain and relaxation. Methods: A retrospective review of 198 children with thermal injury admitted to an American Burn Association-verified pediatric burn center between January 2022 and July 2023 was conducted, excluding those requiring intensive care admission. Demographics, injury details, and MT variables were summarized using descriptive statistics. A logistic regression explored the impact of age, length of stay (LOS), and total body surface area on MT provision. Results: All patients received MT consultation, with 13.6% of patients (n = 27) undergoing 43 MT sessions, with a median duration of 25.0 min. Common burn mechanisms in the MT group were scalds (55.6%), flame (22.2%), and contact (14.8%) burns. Of patients reporting pain pre-massage, 75.0% experienced pain relief, and 95.3% were content, relaxed or resting comfortably post-intervention. Barriers to MT included patients being asleep (42.1%), off the unit (33.7%), or attended to by other health-care providers (21.1%). Patients receiving MT had a longer median LOS compared to those who did not (p < 0.001). Conclusion: MT is potentially valuable for children admitted with acute burns, reducing pain and promoting relaxation. However, patients admitted on weekends and with short admissions frequently missed MT treatment. Addressing barriers through additional weekend resources, provider education, and increased awareness of patient readiness for sessions may improve access to MT.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".