Integrative Oncology Education: An Emerging Competency for Oncology Providers
Bibliographic record
Abstract
A growing number of cancer patients use complementary and alternative therapies during and after conventional cancer treatment. Patients are often reluctant to discuss these therapies with their oncologist, and oncologists may have limited knowledge and confidence on how to advise patients on the appropriate use. Integrative oncology is a patient-centered, evidence-informed field that utilizes mind-body practices, lifestyle modifications and/or natural products interwoven with conventional cancer treatment. It prioritizes safety and best available evidence to offer appropriate interventions alongside conventional care. There are few opportunities for oncologists to learn about integrative oncology. In this commentary, we highlight the Integrative Oncology Scholars (IOS) program as a means to increase competency in this growing field. We provide an overview of several integrative oncology modalities that are taught through this program, including lifestyle modifications, physical activity, and mind-body interventions. We conclude that as more evidence is generated in this field, it will be essential that oncology healthcare providers are aware of the prevalent use of these modalities by their patients and cancer centers include Integrative Oncology trained physicians and other healthcare professionals in their team to discuss and recommend evidence-based integrative oncology therapies alongside conventional cancer treatments to their patients.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".