The Efficacy of Exercise Therapy in Reducing Shoulder Pain Related to Breast Cancer: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Recent research indicates that physiotherapy interventions, such as exercise and manual therapy, may be effective in decreasing the frequency of side effects linked with breast cancer treatment, including fatigue, pain, nausea, and decreased quality of life. This systematic review aims to determine the efficacy of exercise therapy in reducing shoulder pain related to breast cancer treatment and to identify outcome measures that can be used to assess shoulder pain in this population. METHODS: A systematic review of the current literature was conducted using portals such as the Physiotherapy Evidence Database (PEDro), the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, Ovid MEDLINE (1996 to April 2011), and Allied and Complementary Medicine (AMED) (1985 to April 2011). Databases were searched for relevant studies published up to April 2011. Participants in relevant studies were adults (≥18 years of age) with a primary diagnosis of breast cancer at any point during the treatment of their disease. RESULTS: Six articles were independently appraised by two blinded reviewers. Six studies met the inclusion criteria, each analyzing different types of exercise-shoulder/arm/scapular strengthening/stabilization, postural exercises, general exercises and conditioning, shoulder range-of-motion exercises, and lymphedema exercises-with respect to their efficacy in reducing shoulder pain related to breast cancer treatment. CONCLUSIONS: RESULTS suggest that exercise targeting shoulder pain related to breast cancer treatment may be effective. However, definitive conclusions cannot be drawn due to the lack of methodological quality and homogeneity of the studies included. Clinicians should use valid outcome measures, such as the visual analogue scale and brief pain inventory, to evaluate the effectiveness of this treatment.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".