Naturopathic Oncology Care for Thoracic Cancers: A Practice Survey
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is a lack of information on therapies recommended by naturopathic doctors (NDs) for lung and gastroesophageal cancer care. Study objectives were to: (1) identify the most common interventions considered for use by NDs; (2) identify interventions NDs recommend to support key therapeutic goals; and (3) identify potential contraindications between integrative and conventional therapies. METHODS: Oncology Association of Naturopathic Physicians (OncANP) members (n = 351) were invited to complete an electronic survey. Respondents provided information on interventions considered for thoracic cancer pre- and postoperatively across 4 therapeutic domains (supplemental natural health products, physical, mental/emotional, and nutritional), therapeutic goals, and contraindications. This survey was part of the development of the Thoracic Perioperative Integrative Surgical Evaluation trial. RESULTS: Forty-four NDs completed the survey (12.5% response rate), all of whom were trained at accredited colleges in North America and the majority of whom were Fellows of the American Board of Naturopathic Oncology (FABNO) (56.8%). NDs identified significantly more interventions in the postoperative compared to preoperative setting. The most frequently identified interventions included modified citrus pectin, arnica, omega-3 fatty acids, vitamin D, probiotics, exercise, acupuncture, meditation, stress reduction, low glycemic index diet, and Mediterranean diet. Potential contraindications with conventional treatment (surgery, chemotherapy, radiotherapy) differed across natural health products. CONCLUSIONS: These findings highlight naturopathic interventions with a high level of use in thoracic cancer care, describe and characterize therapeutic goals and the interventions used to achieve these goals, and provide insight on how practice changes relative to conventional cancer treatment phase.
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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 | 0.001 |
| 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".