A Population-Based Survey of Complementary and Alternative Medicine Use in Men Recently Diagnosed with Prostate Cancer
Bibliographic record
Abstract
PURPOSE: To determine prevalence and patterns of use of complementary and alternative medicine (CAM) among men recently diagnosed with prostate cancer. STUDY DESIGN: Men, diagnosed with prostate cancer over a 10-month period in British Columbia, Canada, were randomly selected to obtain a population-based sample. METHODS: Surveys, addressing patient demographics, types of CAM therapies, and CAM information resources utilized, reasons for use, and disclosure to physician(s), were mailed to 1108 men newly diagnosed with prostate cancer. A 42% response rate was obtained. RESULTS: Thirty-nine percent of patients used CAM therapies with the most common being herbal supplements (saw palmetto), vitamins (vitamin E), and minerals (selenium). The most common reasons given for choosing to use CAM therapies were to (1) boost the immune system and (2) prevent recurrence. The majority of men (58%) had told their physician(s) about their CAM use, but few utilized either their family physician (15%) or their oncologist (7%) as sources of CAM information. CAM users most commonly consulted friends or family (39%) or the Internet (19%) for information about CAM. CAM users were more likely than nonusers to delay (9%) or decline (4%) conventional treatment. Respondents who had never used CAM had typically never thought about it or did not have enough information about the treatments. CONCLUSIONS: More than one third of recently diagnosed prostate cancer patients utilize some form of CAM therapy, and the majority disclose their use to their physician(s). However, they tend to rely on anecdotal information for their CAM decision making. Dissemination of reliable CAM information is one key to helping men navigate this difficult arena.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".