The Effect of Exercise Training on Breast Cancer Patients: A Review
Bibliographic record
Abstract
Breast cancer is a leading cause of death among women in the United States, with a rate of more than 40,000 deaths annually. In Canada, breast cancer is the third most common cancer. The majority of breast cancer patients encounter a wide range of symptoms and side effects after current treatments including mastectomy, chemotherapy, radiotherapy, and endocrine therapy. Negative health concerns following treatment include muscle loss, proximal muscle weakness, lymphedema, decreased range of motion, and reduced aerobic capacity, all which lead to a decrease in quality of life (QoL). Rehabilitation and exercise programs have been increasingly implemented by hospitals as an effective nonpharmacologic strategy to mitigate side effects of cancer treatments. Given that physical activity may be difficult for a patient to self-incorporate into their daily routine posttreatment, the literature demonstrates that exercise interventions might serve as an efficient approach to facilitate their return to normal life. Many studies in the last 5 yr discuss the efficacy of progressive resistance training, aerobic training, and/or maximal strength training in breast cancer survivors. This review investigates various types of exercise intervention programs to gain better insight into their effectiveness for improving the level of physical functioning and health-related QoL of breast cancer patients during and after treatments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".