Herbal medicines: challenges in the modern world. Part 4. Canada and United States
Bibliographic record
Abstract
INTRODUCTION: Similar to other nations North American people used herbs for thousands of years to treat diseases and purify their spirits. By the middle of the 1900s, evidence-based conventional medicine received wide acceptance in Canada and the United States (US). Nowadays, people are going back to their roots and actively using herbal medicines (HMs) and natural health products (NHPs). Areas covered: This article is focusing on use and regulation of the HMs and NHPs in Canada and the US, raises concerns regarding HM and NHP safety and efficacy, offers suggestions on how to overcome these problems. Materials available from legislative and governmental websites, PubMed and news media were used. Expert commentary: Use of HMs, especially dietary supplements is widespread among adults in Canada and US. HMs and NHPs are regulated in both countries, but minimum criteria for product approval and post-market surveillance have been set. Concerns of quality, contamination, adulteration, and efficacy in are of central importance in the discussion of HMs and NHPs. Detailed product description and research are of vital importance to ensure safety and efficacy of these products. Additionally, 'herbal' education of healthcare providers and patients is needed to guarantee further successful integration of HM and conventional medicines.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".