How do government regulations influence the ability to practice Chinese herbal medicine in western countries
Bibliographic record
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE: The regulation policies of substances used in Chinese Herbal Medicine (CHM), have a direct influence on the ability of health providers to practice in the clinic. AIM OF THE STUDY: We set out to assess the truth behind the assumption that practice of CHM in the west is constrained by the regulations imposed by authorities in western countries. MATERIALS AND METHODS: For the first part of our study we surveyed and compiled lists of banned and restricted Chinese Materia Medica (CMM) from six countries: USA, UK, Germany, Israel, Canada and Australia. Afterwards, we estimated the relevant importance of the 300 CMM most-commonly-prescribed to the practice of CHM according to prescriptions from 2,000,000 randomly selected patients, from the Taiwanese National Health Insurance Research Database (NHIRD). We then compared both lists and determined the clinical importance of the banned and restricted CMM. RESULTS: Except for regulations from Canada, most of the information of banned CMM proved to be difficult to organize. The USA was found to have the least amount of banned herbs, with 9 substances. Canada had the highest amount, with 98. In Germany, Australia, the UK, and Israel 10, 29, 36, 68 banned CMM were found, respectively. Apart from aristolochic acid containing substances, ma huang (, Ephedra sinica) was the only CMM banned in all countries. Most of the banned CMM were not found to be among the most-commonly-prescribed according to the NHIRD. CONCLUSION: Authorities should make this information more accessible. No clear relation exists between CHM regulations and any 'Western' common denominator, and the amount of banned CMM varied greatly among the surveyed countries. However, even among countries with a larger amount of banned CMM, the majority of these were in the bottom two-thirds in respect to the frequency of their use. Thus, regulations in some western countries surely influence the practice of CHM, however, the variability of CMM have been influenced by regulations only to a limited extent.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".