Addressing Complementary and Alternative Medicine Use Among Individuals With Cancer: An Integrative Review and Clinical Practice Guideline
Bibliographic record
Abstract
Complementary and alternative medicine (CAM) use is common among individuals with cancer, but many choose not to discuss CAM with health-care providers (HCPs). Moreover, there is variability in the provision of evidence-informed decision making about CAM use. A clinical practice guideline was developed to standardize how oncology HCPs address CAM use as well as to inform how individuals with cancer can be supported in making evidence-informed decisions about CAM. An integrative review of the literature, from inception to December 31, 2018, was conducted in MEDLINE, EMBASE, PsychINFO, CINAHL, and AMED databases. Eligible articles included oncology HCPs' practice related to discussing, assessing, documenting, providing decision support, or offering information about CAM. Two authors independently searched the literature, and selected articles were summarized. Recommendations for clinical practice were formulated from the appraised evidence and clinical experiences of the research team. An expert panel reviewed the guideline for usability and appropriateness and recommendations were finalized. The majority of the 30 studies eligible for inclusion were either observational or qualitative, with only 3 being reviews and 3 being experimental. From the literature, 7 practice recommendations were formulated for oncology HCPs regarding how to address CAM use by individuals with cancer, including communicating, assessing, educating, decision coaching, documenting, active monitoring, and adverse event reporting. It is imperative for safe and comprehensive care that oncology HCPs address CAM use as part of standard practice. This clinical practice guideline offers directions on how to support evidence-informed decision making about CAM among individuals 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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".