Complementary and Alternative Medicine: A Survey of Its Use in Pediatric Oncology
Bibliographic record
Abstract
Background. The use of complementary and alternative medicine (CAM) is high among children and youths with chronic illnesses, including cancer. The objective of this study was to assess prevalence and patterns of CAM use among pediatric oncology outpatients in two academic clinics in Canada. Procedure. A survey was developed to ask patients (or their parents/guardians) presenting to oncology clinics at the Stollery Children's Hospital in Edmonton and the Children's Hospital of Eastern Ontario (CHEO) in Ottawa about current or previous use of CAM products and practices. Results. Of the 137 families approached, 129 completed the survey. Overall CAM use was 60.5% and was not significantly different between the two hospitals. The most commonly reported reason for not using CAM was lack of knowledge about it. The most common CAM products ever used were multivitamins (86.5%), vitamin C (43.2%), cold remedies (28.4%), teething remedies (27.5%), and calcium (23.0%). The most common CAM practices ever used were faith healing (51.0%), massage (46.8%), chiropractic (27.7%), and relaxation (25.5%). Many patients (40.8%) used CAM products at the same time as prescription drugs. Conclusion. CAM use was high among patients at two academic pediatric oncology clinics. Although most respondents felt that their CAM use was helpful, many were not discussing it with their physicians.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".