Effect of myofascial techniques for treatment of persistent arm pain after breast cancer treatment: randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of myofascial therapy in addition to a standard physical therapy program for treatment of persistent arm pain after finishing breast cancer treatment. DESIGN: Double-blinded (patient and assessor) randomized controlled trial. SETTING: University Hospitals Leuven, Belgium. PATIENTS: A total of 50 patients with persistent arm pain and myofascial dysfunctions after breast cancer treatment. INTERVENTION: Over three months, all patients received a standard physical therapy program. The intervention group received in addition 12 sessions of myofascial therapy, and the control group received 12 sessions of placebo therapy. MAIN MEASUREMENTS: ) and pain quality (McGill Pain Questionnaire). Measures were taken before and after the intervention and at long term (6 and 12 months follow-up). RESULTS: Patients in the intervention group had a significantly greater decrease in pain intensity compared to the control group (VAS -44/100 vs. -24/100, P = 0.046) with a mean difference in change after three months between groups of 20/100 (95% confidence interval, 0.4 to 39.7). After the intervention, 44% versus 64% of patients still experienced pain in the intervention and control group, respectively ( P = 0.246). No significant differences were found for the other outcomes. CONCLUSION: Myofascial therapy is an effective physical therapy modality to decrease pain intensity at the arm in breast cancer survivors at three months, but no other benefits at that time were found. There were no long-term effects at 12 months either.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".