Global Use of Traditional and Complementary Medicine in Childhood Cancer: A Systematic Review
Bibliographic record
Abstract
Purpose Traditional and complementary medicine (T&CM) strategies are commonly used in pediatric oncology. Patterns may vary based on country income. We systematically reviewed published studies describing T&CM use among pediatric oncology patients in low-income countries (LIC/LMIC), middle-income countries (UMIC), and high-income countries (HIC). Objectives included describing estimated prevalence of use, reasons for use, perceived effectiveness, modalities used, rates of disclosure, and reporting of delayed or abandoned treatment. Methods MEDLINE, EMBASE, Global Health, CINAHL, PsycINFO, Allied and Complementary Medicine Database, Cochrane Database of Systematic Reviews, and ProceedingsFirst were searched. Inclusion criteria were primary studies involving children younger than the age of 18 years, undergoing active treatment of cancer, and any T&CM use. Exclusion criteria included no pediatric oncology-specific outcomes and studies involving only children off active treatment. Data were extracted by two reviewers using a systematic data extraction form determined a priori. Results Sixty-five studies published between 1977 and 2015 were included, representing 61 unique data sets and 7,219 children from 34 countries. The prevalence of T&CM use ranged from 6% to 100%. Median rates of use were significantly different in LIC/LMIC (66.7% ± 19%), UMIC (60% ± 26%), and HIC (47.2% ± 20%; P = .02). Rates of disclosure differed significantly by country income, with higher median rates in HIC. Seven studies reported on treatment abandonment or delays. Conclusion The use of T&CM in pediatric oncology is common worldwide, with higher median prevalence of use reported in LIC/LMIC. Further research is warranted to examine the impact on treatment abandonment and delay.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".