Acupuncture for Treating Common Side Effects Associated With Breast Cancer Treatment: A Systematic Review
Bibliographic record
Abstract
Background: Although breast cancer treatment is associated with improved survival rates, it is also associated with numerous side effects, which can decrease overall quality of life for patients. Recent research indicates acupuncture may be useful in decreasing the incidence and duration of some side effects associated with cancer treatment. Objective: To assess the evidence surrounding the role of acupuncture in treating side effects associated with breast cancer treatment. Design: Systematic review based on search of PubMed, EMBASE (1996 to 2009 week 17), AMED (1985 to April 2009), and Ovid MEDLINE (1996 to April 2009) databases for relevant studies published up to April 2009. Authors of recent studies were contacted to determine if additional studies were taking place. Fourteen articles were independently appraised by 4 blinded reviewers. Results: Twelve studies met inclusion criteria: 9 investigated effects of traditional acupuncture and 3 addressed electroacupuncture. Seven different side effects were examined (hot flashes, fatigue, pain, dyspnea, psychological well-being, decreased range of motion with lymphedema, and emesis). The findings support the potential use of traditional acupuncture to decrease hot flashes, fatigue, and pain, whereas electroacupuncture may be useful in treating emesis and hot flashes. There is a paucity of high-quality evidence to support the use of acupuncture to treat dyspnea, emesis, and decreased range of motion with lymphedema or to improve psychological well-being. Conclusion: Current evidence suggests that traditional acupuncture may be useful in reducing hot flashes, fatigue, and pain, whereas electroacupuncture may be useful in treating emesis and hot flashes. Due to limitations in study designs and heterogeneity in treatment protocols, results should be viewed with caution and combined with clinical reasoning.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| 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".