Complementary and Alternative Medicine Use in Patients Before and After a Cancer Diagnosis
Bibliographic record
Abstract
Background: Cancer patients are increasingly seeking out complementary and alternative medicine (cam) and might be reluctant to disclose its use to their oncology treatment team. Often, cam agents are not well studied, and little is known about their potential interactions with chemotherapy, radiation therapy, or biologic therapies, and their correlations with outcomes. In the present study, we set out to determine the rate of cam use in patients receiving treatment at a Northern Ontario cancer centre. Methods: Patients reporting for treatment at the Northeast Cancer Centre (necc) in Sudbury, Ontario, were asked to complete an anonymous questionnaire to assess cam use. Changes in cam use before, compared with after, diagnosis were also assessed. Results: Patients in Northern Ontario reported significant cam use both before and after diagnosis. However, as a function of the cam type, cam use was greatly enhanced after cancer diagnosis. For example, the number of patients who reported use of biologic products increased to 51.8% after a cancer diagnosis from 15.6% before a cancer diagnosis. Patients reported much smaller changes in the use of alternative medical systems or spiritual therapy after diagnosis. Vitamin use was reported by 66% of respondents, and the number of different cams used correlated significantly with the reported number of vitamins used. Conclusions: Use of cam, particularly biologic products, increased significantly after a cancer diagnosis. Further studies are required to examine the effect of cam use on the efficacy and safety of cancer therapies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".