Editorial: Drug development of herbal medicines: Regulatory perspectives
Bibliographic record
Abstract
Drug development of herbal medicines: Regulatory perspectivesThe global trade of medicinal plants and their derivatives was estimated at US$33 billion in 2014, and the World Health Organization has estimated that it will increase to US$50 trillion by 2050.Different regulatory frameworks and categories at the national and regional levels describe medicinal plants either as mainstream therapy or as complementary and alternative medicines.The resulting complex terminology has seen medicinal plants classified as medicines (Australia), herbal medical products (European Union), botanicals (United States), and natural health products (Canada).In China, there is a distinction between traditional Chinese medicine (TCMs) and natural medicinal products.In India, traditional medicine is separated into three systems: Ayurveda, Unani, and Siddha.In Japan, Kampo medicines are classified as pharmaceutical drugs, and in Thailand, as part of the primary health care system.Many national health authorities have established guidelines and regulations concerning the quality, efficacy, and safety profiles of these products.Five papers are included in this Research Topic, all of which concern these three fundamental aspects of the health properties of herbal medicines.The paper by Chen et al. discusses the need to develop quality control systems to evaluate TCMs by assessing the quality control measures used for Glehniae Radix, a medicinal plant, along its value chains (VCs).Glenhae Radix was chosen as a "model" plant material due to its constantly increasing global demand, especially in Asian countries.Previous studies have shown that the production and processing methods of different VCs impact the quality of the medicinal materials.Four years of field and market research were conducted for the study, including interviews with stakeholders in the VCs.Different types of VCs were integrated and further analyzed.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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".