How Many Cancer Patients Use Complementary and Alternative Medicine
Bibliographic record
Abstract
BACKGROUND: No comprehensive systematic review has been published since 1998 about the frequency with which cancer patients use complementary and alternative medicine (CAM). METHODS: MEDLINE, AMED, and Embase databases were searched for surveys published until January 2009. Surveys conducted in Australia, Canada, Europe, New Zealand, and the United States with at least 100 adult cancer patients were included. Detailed information on methods and results was independently extracted by 2 reviewers. Methodological quality was assessed using a criteria list developed according to the STROBE guideline. Exploratory random effects metaanalysis and metaregression were applied. RESULTS: Studies from 18 countries (152; >65 000 cancer patients) were included. Heterogeneity of CAM use was high and to some extent explained by differences in survey methods. The combined prevalence for "current use" of CAM across all studies was 40%. The highest was in the United States and the lowest in Italy and the Netherlands. Metaanalysis suggested an increase in CAM use from an estimated 25% in the 1970s and 1980s to more than 32% in the 1990s and to 49% after 2000. CONCLUSIONS: The overall prevalence of CAM use found was lower than often claimed. However, there was some evidence that the use has increased considerably over the past years. Therefore, the health care systems ought to implement clear strategies of how to deal with this. To improve the validity and reporting of future surveys, the authors suggest criteria for methodological quality that should be fulfilled and reporting standards that should be required.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".