Complementary and Alternative Medicine Use and Breast Cancer Prognosis: A Pooled Analysis of Four Population-Based Studies of Breast Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Complementary and alternative medicine (CAM) use is common among breast cancer survivors, but little is known about its impact on survival. METHODS: We pooled data from four studies conducted in Hawaii in 1994-2003 and linked to the Hawaii Tumor Registry to obtain long-term follow-up information. The effect of CAM use on the risk of breast cancer-specific death was evaluated using Cox regression. RESULTS: The analysis included 1443 women with a median follow-up of 11.8 years who had a primary diagnosis of in situ and invasive breast cancer. The majority were Japanese American (36.4%), followed by white (26.9%), Native Hawaiian (15.9%), other (10.6%), and Filipino (10.3%). CAM use was highest in Native Hawaiians (60.7%) and lowest in Japanese American (47.8%) women. Overall, any use of CAM was not associated with the risk of breast cancer-specific death (hazard ratio [HR] 1.47, confidence interval [CI] 0.91-2.36) or all-cause death (HR 0.82, 95% CI 0.63-1.06). However, energy medicine was associated with an increased risk of breast cancer-specific death (HR 3.19, 95% CI 1.06-8.52). When evaluating CAM use within ethnic subgroups, Filipino women who used CAM were at increased risk of breast cancer death (HR 6.84, 95% CI 1.23-38.19). CONCLUSIONS: Our findings suggest that, overall, CAM is not associated with breast cancer-specific death but that the effects of specific CAM modalities and possible differences by ethnicity should be considered in future studies.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".