Bibliographic record
Abstract
As the evidence surrounding natural products in cancer care develops, there is an urgent need to translate and transfer this emerging knowledge to point-of-care practitioners as well as patients and families. In addition, standardization of assessment and documentation of natural product use is required to support informed decision making, and to avoid potential negative interactions with conventional cancer treatments. During this presentation, the development and evaluation of innovative knowledge synthesis, translation and transfer strategies will be discussed, including clinical practice guidelines, decision aids, and patient and provider education programs aimed at enhancing communication, decision making and care related to natural product utilization in cancer care. Included in the discussion will be the findings of the Complementary Medicine Education and Outcomes research program, and the MyChoices decision aid project. Two guidelines, the Society for Integrative Oncology's Clinical Practice Guidelines on the Use of Integrative Therapies as Supportive Care in Patients treated for Breast Cancer and the Complementary and Integrative Medicine Best Practice Guideline will also be reviewed. Challenges related to evidence dissemination and uptake related to natural products in cancer care will be considered and recommendations regarding future knowledge translation and transfer activities will be shared.
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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 | 0.069 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".