Can Complementary and Alternative Medicine Clinical Cancer Research Be Successfully Accomplished? The Mayo Clinic–North Central Cancer Treatment Group Experience
Bibliographic record
Abstract
Some critics question whether research on complementary and alternative modalities for patients with cancer can be done efficiently in traditional clinical settings. This article reviews a program of complementary medicine research that has been done in a traditional clinical setting over the past 30 years. Trials using complementary therapies for both symptom management and cancer treatment done by the Mayo Clinic and the North Central Cancer Treatment Group are reviewed. Twenty-seven studies have been developed using complementary therapies, addressing such issues as mucosal and epidermal toxicity, hot flashes, lymphedema, anorexia and cachexia, insomnia, cognitive dysfunction, fatigue, and cancer treatment. Nineteen of them have been completed and have had results published in peer-reviewed clinical journals, whereas two manuscripts are in press. Two other trials have recently completed accrual, and the data are being analyzed so that manuscripts can be prepared. In addition, four clinical trials are actively accruing patients. The data presented in this article demonstrate that complementary and alternative medicine research can be done in a scientifically sound manner. Well-designed and adequately powered studies can be implemented, and large numbers of patients can be accrued. The resulting research evaluations can be published in peer-reviewed medical journals.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".