Regulatory frameworks and evidence requirements for traditional, complementary and integrative medicines; Australia, Canada, China, Republic of Korea, the United States of America and the European Union
Bibliographic record
Abstract
Traditional, complementary and integrative medicine plays an important role in global health-care systems. Despite its widespread use and recognition by more than 170 Member States of the World Health Organization, many disparities in regulation exist between countries. We conducted a comparative analysis of the regulatory frameworks governing traditional medicine products in six high- or middle-income countries or jurisdictions where traditional medicine is used extensively: Australia, Canada, China, Republic of Korea, United States of America and the European Union. We focused on marketing authorization pathways, approval standards and successful approvals. We found differences in regulatory approaches, with countries adopting either clinical study-based or traditional knowledge-based pathways which led to varying requirements for non-clinical and clinical evidence. While the European Union and the United States acknowledge historical human-use evidence, relatively rigorous clinical investigations are required. Australia and Canada consider historical human-use evidence in marketing authorization for products that do not require professional supervision. Recent regulatory reforms in countries such as China and the Republic of Korea aim to enhance regulatory supervision. Across all jurisdictions, fluctuations in the number of successful applications persisted amid evolving policy changes and regulatory requirements. To promote the worldwide use of traditional medicine products, a globally coordinated, tiered and risk-based international framework is needed to ensure the efficacy, quality and safety of traditional medicine products. This approach requires establishing stable (i.e. predictable and consistently implemented) regulatory systems, strengthening the evidence on traditional medicine products with both clinical and real-world data, and facilitating regulatory convergence through reciprocity and globally harmonized evaluation standards.
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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 | 0.343 | 0.445 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".