International Survey on the Use of Complementary and Alternative Medicines for Common Toxicities of Radiation Therapy
Bibliographic record
Abstract
Complementary and alternative medicines (CAMs) are widely used by patients with cancer. However, little is known about the extent to which these potential remedies are used internationally to treat the most common toxicities of radiation therapy. We report on the results of an international survey that assessed the use of CAMs. Surveys were distributed to 1174 practicing radiation oncologists. Questions evaluated the perceptions of CAMs and specific practice patterns for the use of CAM remedies in the treatment of common radiation-induced toxicities (eg, skin, fatigue, nausea, diarrhea, and mucositis/xerostomia). The responses were compared between the groups using the χ2 test and stratified on the basis of provider location, number of years in practice, and perception of CAMs. A total of 114 radiation oncologists from 29 different countries completed the survey, with a balanced distribution between North American (n = 56) and non-North American (n = 58) providers. Among the responding clinicians, 63% recommended CAMs in their practice. The proportion of clinicians who recommend CAMs for radiation toxicities did not significantly vary when stratified by provider’s number of years in practice (P = .23) or location (United States/Canada vs other; P = .74). Overall, providers reported that 29.4% of their patients use CAMs, and 87.7% reported that their practice encouraged or was neutral on CAM use, whereas 12.3% recommended stopping CAMs. The most common sources of patient information on CAMs were the Internet (75.4%), friends (60.5%), and family (58.8%). Clinicians reported the highest use of CAMs for radiation skin toxicity at 66.7%, followed by 48.2% for fatigue, 40.4% for nausea, and 36.8% for mucositis/xerostomia. Nearly two-thirds of the surveyed radiation oncologists recommend CAMs for radiation-related toxicities; however, they estimated that less than one third of patients use CAMs for this purpose. This suggests a need for further investigation and perhaps greater patient education on the roles of CAMs in treating radiation toxicities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".