Advising Patients Who Seek Complementary and Alternative Medical Therapies for Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.001 |
| 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 it