Complementary and alternative medicine and other health behaviors.
Bibliographic record
Abstract
23 Background: Complementary and alternative medicine (CAM) use in patients with cancer has increased. A patient’s decision to seek CAM alongside conventional cancer treatment is complex. We evaluated whether patients who sought CAM were also more likely to engage in other healthy behaviours such as exercise, smoking cessation, alcohol reduction, and maintaining a healthy weight. Methods: As part of a larger survey of cancer survivors, 551 cancer patients across Princess Margaret Cancer Centre (Canada) were queried on clinico-demographic information, their use of CAM and other health-related behaviors (smoking, alcohol use, healthy weight, etc.). Multivariable logistic regression assessed each health behavior, adjusting for clinical factors associated with CAM use. Results: Females: 53%; median age: 54 years; Caucasian: 83%. Primary tumor sites: breast/gynecologic 22%; gastrointestinal/genitourinary 28%; hematologic 23%; lung/head and neck 12%. Following their cancer diagnosis, 43% used CAM. Being female (odds ratio=2.55, 95% CI [1.8-3.7], having higher education (2.08 [1.4-3.1]) or higher income (1.80 [1.2-2.7]), and having breast/gynaecological cancers (vs. all others; 2.82 [1.8-4.3]) were associated with greater CAM use. These factors served as adjustment variables for the analysis of behaviors. Behaviors associated with increased use of CAM included: use of CAM prior to diagnosis (10.6 [6.5-17.2]), participation in support groups (3.39 [2.1-5.6]), not being overweight or obese one year prior to diagnosis (1.82 [1.2-2.7]), and meeting Canadian physical activity guidelines either before diagnosis (1.80 [1.2-2.8]) or currently (1.70 [1.0-2.8]). No association was observed between CAM use and smoking status or cessation, alcohol intake or reduction, self-described diet habits prior to cancer diagnosis or dietary changes after diagnosis. Conclusions: Some behaviors such as baseline and current physical activity, participation in support groups, not being overweight, and prior use of CAM were each associated with greater CAM use. Smoking, alcohol and diet were not associated with CAM use. Improved understanding of the reasons for CAM use can an improve patient-physician communication, decision-making, and treatment planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.004 |
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".