Complementary and alternative medicine (CAM) use in advanced cancer: a systematic review
Bibliographic record
Abstract
This systematic review synthesizes knowledge about the use of complementary and alternative medicine (CAM) among advanced cancer patients. EBSCO and Ovid databases were searched using core concepts, including advanced cancer, CAM, integrative medicine, and decision-making. Articles included in the final review were analyzed using narrative synthesis methods, including thematic analysis, concept mapping, and critical reflection on the synthesis process. Results demonstrate that advanced cancer patients who are younger, female, more educated, have longer duration of disease, and have previously used CAM are more likely to use CAM during this stage of illness. Key themes identified include patterns of and reasons for use; and barriers and facilitators to informed CAM decision-making. Knowledge regarding the use of CAM in advanced cancer remains in its nascent stages. Findings suggest a need for more research on understanding the dynamic process of CAM decision-making in the advanced cancer population from the patients' perspective. U p to 93.1% of people report using some form of complementary and alternative medicine (CAM) during their cancer experience. Alt ugh the use of CAM in the general cancer population has been well documented over the past two decades, The diagnosis of advanced cancer represents a shift in the focus of treatment and care from cure to palliation. Advancedcancer patients have reported increased levels of distress and symptom burden with poorer quality of life compared to those with curative or early stage disease. Given the complexity in caring for people with advanced cancer and a goal to provide evidence-informed CAM decision support, understanding the unique CAMrelated needs of this population will be valuable to oncology health care providers (HCPs). In this review, we seek to describe the factors, reasons, and decision-making process used by advanced-cancer patients to use CAM.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 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 | 0.001 |
| 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".