Naturopathic Oncology Care for Pediatric Cancers: A Practice Survey
Bibliographic record
Abstract
Background: The majority of pediatric oncology patients report use of complementary and alternative medicine. Some naturopathic doctors (NDs) provide supportive pediatric oncology care; however, little information exists to formally describe this clinical practice. A survey was conducted with members of the Oncology Association of Naturopathic Physicians (OncANP.org) to describe recommendations across four therapeutic domains: natural health products (NHPs), nutrition, physical medicine, and mental/emotional support. Results: We had 99 respondents with a wide variance of clinical experience and aptitude to treat children with cancer. Of the majority (52.5%) of respondents who choose not to treat these children, the three primary reasons for this are lack of public demand (45.1%), institutional or clinic restrictions (21.6%), and personal reasons/comfort (19.6%). The 10 most frequently considered NHPs by all NDs are fish-derived omega-3 fatty acid (83.3%), vitamin D (83.3%), probiotics (82.1%), melatonin (73.8%), vitamin C (72.6%), homeopathic Arnica (69.0%), turmeric/curcumin (67.9%), glutamine (66.7%), Astragalus membranaceus (64.3%), and Coriolus versicolor/PSK (polysaccharide K) extracts (61.9%). The top 5 nutritional recommendations are anti-inflammatory diets (77.9%), dairy restriction (66.2%), Mediterranean diet (66.2%), gluten restriction (61.8%), and ketogenic diet (57.4%). The top 5 physical modality interventions are exercise (94.1%), acupuncture (77.9%), acupressure (72.1%), craniosacral therapy (69.1%), and yoga (69.1%). The top 5 mental/emotional interventions are meditation (79.4%), art therapy (77.9%), mindfulness-based stress reduction (70.6%), music therapy (70.6%), and visualization therapy (67.6%). Conclusion: The results of our clinical practice survey highlight naturopathic interventions across four domains with a strong rationale for further inquiry in the care of children with cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".