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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".