Complementary and Alternative Medicines and Patients With Breast Cancer: A Case of Mortality and Systematic Review of Patterns of Use in Patients With Breast Cancer
Bibliographic record
Abstract
BACKGROUND: The use of complementary and alternative medicines (CAMs) is common among women being treated for breast cancer. A recent mortality associated with CAM at our center precipitated a systematic review of the Cochrane, EMBASE, and PubMed databases to identify English manuscripts including "CAM" and "breast cancer." METHODS: Papers included for review were selected based on predefined inclusion and exclusion criteria. The primary outcome was the use of CAM by women with breast cancer. Secondary outcomes included timing of use along disease trajectory, attitudes toward CAM by allopathic practitioners, and patient disclosure of CAM use to treating allopathic physicians. RESULTS: Of 701 titles identified by the search strategy, 36 met the inclusion criteria. The weighted average proportion of women with breast cancer who use CAM was 40% (standard deviation: 18%). The diagnosis of breast cancer also prompts the initiation or increase of CAM use. However, up to 84% of patients do not disclose the use of CAM to their allopathic practitioners. CONCLUSIONS: Although CAM is often dismissed as a harmless addition to allopathic therapy, significant complications and interactions can occur. Our review and the dramatic case example provided highlight the need for physicians to educate themselves regarding CAM and to engage with their patients regarding its use.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".