THE TRADITIONAL USE AS A REGULATORY CATEGORY – EXPERIENCES IN EUROPE
Bibliographic record
Abstract
The new European legislation introduced with the Directive 2004/24/EC amending Directive 2001/83/EC a harmonised registration scheme for traditional Herbal Medicinal Products (HMPs) at multinational level. With this legislation the Traditional Use (TU) of HMPs gained a new meaning, because the proof of traditions may replace individual product data for efficacy and safety. With the pan-European upgrade from a simply historic category to a legal one, the TU is now part of the regulatory strategy for HMP manufacturers and as such unique in the licensing of pharmaceuticals. We compared the European legal basis for traditional HMPs with the former legal practice by some European countries (France, Germany, Hungary, Spain) and also non-European countries (Australia, Brazil, Canada, India) in terms of six major criteria: (I) self medication character, (II) specified strength and posology, (III) appropriate route of administration, (IV) period of traditional use, (V) sufficient data on safety, and (IV) plausibility of pharmacological effects. Examples (e.g. Equisetum, Solidago, Echinacea). For the acceptance of evidence for Community monographs, the adjustment with monographs of the European Pharmacopoeia, and experience from referral cases are presented. In addition, the different concepts of (1) well-established use medicinal products and (2) foodstuffs with health claims - the major borderlines within European legislation- are discussed. The experiences of European harmonisation process might be useful for Africa and other regions of more diverse traditions when accepting rationally TU in order to strengthen the position of HMPs on the market.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".