Myofascial Massage for Chronic Pain and Decreased Upper Extremity Mobility After Breast Cancer Surgery
Bibliographic record
Abstract
BACKGROUND: Chronic localized pain and decreased upper extremity mobility commonly occur following breast cancer surgery and may persist despite use of pain medication and physical therapy. PURPOSE: We sought to determine the value of myofascial massage to address these pain and mobility limitations. SETTING: The study took place at a clinical massage spa in the U.S. Midwest. The research was overseen by MetroHealth Medical Center's Institutional Review Board and Case Center for Reducing Health Disparities research staff. PARTICIPANTS: 21 women with persistent pain and mobility limitations 3-18 months following breast surgery. RESEARCH DESIGN: We conducted a pilot randomized controlled trial where intervention patients received myofascial massages and control patients received relaxation massages. INTERVENTION: Intervention participants received 16 myofascial massage sessions over eight weeks that focused on the affected breast, chest, and shoulder areas. Control participants received 16 relaxation massage sessions over eight weeks that avoided the affected breast, chest, and shoulder areas. Participants completed a validated questionnaire at the beginning and end of the study that asked about pain, mobility, and quality of life. MAIN OUTCOME MEASURES: Outcome measures include change in self-reported pain, self-reported mobility, and three quality-of-life questions. RESULTS: = .002) after eight weeks. All intervention and control participants reported that receiving massage treatments was a positive experience. CONCLUSIONS: Myofascial massage is a promising treatment to address chronic pain and mobility limitations following breast cancer surgery. Further work in several areas is needed to confirm and expand on our study findings.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".