Pharmaceutical Regulation of Herbal Medicinal Products in the Countries of the European Union, the USA, Canada and Japan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The regulation of herbal medicines is changing and alters in the different countries. The Federal Food, Drug, and Cosmetic Act (FD&C Act) defines medicines on the grounds of their intended use. Medicines shall be preliminarily approved by the FDA prior to their placing on the market or if they are OTC – they shall meet the requirements of specific regulations, called monographs, for their category. The definition of medicine according to the Canadian Food and Drugs Act (R.S.C., 1985, c. F-27) is “any substance or a combination of substances manufactured, sold or made available for use”. In Japan, the objective of the Medicines and Medical Products Act is to improve public health by means of regulations that are necessary to guarantee the quality, the efficiency and the safety of medicines, quasi-drugs, cosmetics, medical and medicinal products. The definition of a medicinal product in the EU has been specified in Section I Definitions of Directive 83/2001/EC. In the aforementioned countries under consideration, medicines are classified into: medicines prescribed by a doctor (POM) and medicines sold without a doctor’s prescription (OTC). The conducted comparative analysis of the aforementioned countries has shown that there are specific requirements and regulations for herbal medicinal products in the European Union. In the USA and Canada, herbal medicinal products are regarded as a subsection of the Federal Food, Drug, and Cosmetic Act (FD&C Act), sec. 351-360n-1 U.S.C. 379e; the Food and Drugs Act (R.S.C., 1985, c. F-27) – Government of Canada. In the Japanese legislation, there are no specific requirements for herbal medicinal products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it