Integrative medicine in breast cancer survivorship care
Bibliographic record
Abstract
Integrative medicine use is prevalent among breast cancer survivors to improve lifestyle and manage symptoms associated with cancer or its treatment. This review provides an overview of integrative therapies used for lifestyle improvement and symptom management in breast cancer patients, including diet and exercise recommendations, mind-body approaches including mindfulness-based intervention, acupuncture, yoga, massage, and dietary supplements. A balanced diet with an emphasis on plant-based foods and limited consumption of red meat, processed meats, alcohol, and refined sugar is recommended for cancer patients and survivors. Physical activity is also important, with recommendations of 30 minutes of moderate exercise daily, aiming for 150 minutes per week. Mind-body approaches such as meditation, yoga have been shown to improve emotional self-regulation, anxiety, depression, and other symptoms. Acupuncture has demonstrated potential benefits for aromatase inhibitor-induced musculoskeletal symptoms, hot flashes, peripheral neuropathy, and fatigue. Massage may help address pain, anxiety, stress, and improve quality of life (QoL). Supplement use should be approached with caution, especially during active treatment, due to potential interactions with cancer therapies. The review highlights the role of specialized integrative medicine practitioners, such as naturopathic doctors and acupuncturists, in safely incorporating these therapies into standard cancer care. This collaborative approach supports survivors' diverse needs and empowers them to actively participate in their recovery, ultimately improving health outcomes and QoL. Integrative therapy programs within cancer care institutions can provide structure, guidance, and support for patients to safely incorporate these modalities into their treatment and recovery plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".