Advising Patients Who Seek Complementary and Alternative Medical Therapies for Cancer
Bibliographic record
Abstract
Many patients with cancer use complementary and alternative medical (CAM) therapies. Physicians need authoritative information on CAM therapies to responsibly advise patients who seek these interventions. This article summarizes current evidence on the efficacy and safety of selected CAM therapies that are commonly used by patients with cancer. The following major categories of interventions are covered: dietary modification and supplementation, herbal products and other biological agents, acupuncture, massage, exercise, and psychological and mind-body therapies. Two categories of evidence on efficacy are considered: possible effects on disease progression and survival and possible palliative effects. In evaluating evidence on safety, two types of risk are considered: the risk for direct adverse effects and the risk for interactions with conventional treatments. For each therapy, the current balance of evidence on efficacy and safety points to whether the therapy may be reasonably recommended, accepted (for example, dietary fat reduction in well-nourished patients with breast or prostate cancer), or discouraged (for example, high-dose vitamin A supplementation). This strategy allows the development of an approach for providing responsible, evidence-based, patient-centered advice to persons with cancer who seek CAM therapies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".