Complementary and alternative medical treatment of breast cancer: a survey of licensed North American naturopathic physicians.
Bibliographic record
Abstract
CONTEXT: Complementary and alternative medicine (CAM) use is on the rise in the United States, especially for breast cancer patients. Many CAM therapies are delivered by licensed naturopathic physicians using individualized treatment plans. OBJECTIVE: To describe naturopathic treatment for women with breast cancer. DESIGN: Cross-sectional mail survey in 2 parts: screening form and 13-page survey. SETTING: Bastyr University Cancer Research Center, Kenmore, Wash. PARTICIPANTS: All licensed naturopathic physicians in the United States and Canada (N=1,356) received screening forms; 642 (47%) completed the form. Of the respondents, 333 (52%) were eligible, and 161 completed the survey (48%). MAIN OUTCOME MEASURES: Demographics of naturopathic physicians, development of treatment plans, CAM therapies used, perceived efficacy of therapeutic interventions. RESULTS: Of those respondents screened, 497 (77%) had provided naturopathic care to women with breast cancer, and 402 (63%) had treated women with breast cancer in the previous 12 months. Naturopaths who were women were more likely than men to treat breast cancer (P < or = .004). Of the survey respondents, 104 (65%) practiced in the United States, and 57 (35%) practiced in Canada; 107 (66.5%) were women, and 54 (33.5%) were men. To develop naturopathic treatment plans, naturopathic physicians most often considered the stage of cancer, the patient's emotional constitution, and the conventional therapies used. To monitor patients clinically, 64% of the naturopathic physicians used diagnostic imaging, 57% considered the patient's quality of life, and 51% used physical examinations. The most common general CAM therapies used were dietary counseling (94%), botanical medicines (88%), antioxidants (84%), and supplemental nutrition (84%). The most common specific treatments were vitamin C (39%), coenzyme Q-10 (34%), and Hoxsey formula (29%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".