A Comprehensive Description of Naturopathic Care for Advanced Cancers: Outcomes from the Canadian/US Integrative Oncology Study
Bibliographic record
Abstract
Background: There is a paucity of real-world data on the treatments naturopathic doctors (NDs) use for supportive cancer care. We conducted an observational cohort study to comprehensively describe the treatments NDs with experience in cancer care recommend to their patients. Methods: Patients with advanced breast, colorectal, ovarian, or pancreatic cancer were recruited from 12 North American naturopathic clinics and followed for 2 to 3 years. Therapeutic recommendations were abstracted from clinic records. Results: 384 participants (154 breast, 112 colorectal, 71 ovarian, 47 pancreatic) were included in the analyses. The median number of ND visits was 5. The most common types of recommendations were natural health products (NHPs, 99% of participants), nutrition guidance (88%), and parenteral therapies (81%). Mental health (33%) and Traditional Chinese Medicine (29%) were least common. Participants were recommended a median of 11 NHPs throughout the study, with 430 unique products recommended across all participants. Nutrition guidance heavily favoured encouraging foods rather than discouraging them (83% vs. 17% of all dietary recommendations, respectively). Vitamin D, curcumin, intravenous vitamin C, increasing protein intake, and exercise were recommended to at least 50% of participants across each cancer type. Other common recommendations included melatonin, fish oil, Trametes versicolor, subcutaneous mistletoe, increasing vegetable intake, and eating behaviour changes. Recommendations were fairly uniform between cancer types, with the most variation seen in NHPs. Conclusion: NDs frequently recommend NHPs, nutrition guidance, and parenteral therapies for people with advanced cancer. The diversity of unique recommendations suggests individualized care, yet some commonly used treatments demonstrate a degree of consistency.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".