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Record W4310376440 · doi:10.3390/ijerph192315690

Historical Aspects of Herbal Use and Comparison of Current Regulations of Herbal Products between Mexico, Canada and the United States of America

2022· review· en· W4310376440 on OpenAlexaboutno aff
Patricia Rojas, Helgi Jung–Cook, Elizabeth Ruiz-Sánchez, Irma Susana Rojas‐Tomé, Carolina Rojas-Chinchilla, Arely M. López-Ramírez, Aldo Arturo Reséndiz‐Albor

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyGovernment (linguistics)Public healthEnvironmental healthBusinessMedicineTraditional medicineGeographyPopulation

Abstract

fetched live from OpenAlex

Increased life expectancy and high costs of medicines and medical care have led to the use of herbal products. However, these items may contain toxic compounds that have an impact on public health. We will focus on the regulatory aspects and differences of these products marketed in the North American region (USA-Mexico-Canada) from government websites and selected literature. Mexico has an ancestral tradition of using plants for the treatment, improvement, and maintenance of human health as compared with Canada and the USA Currently, the use of herbal products in this region has a regulatory framework. The legal framework in these three countries is related to their history, idiosyncrasies, socio-economic and cultural aspects. Therefore, there are different public policies for herbal products consumed in the region. Mexico has a more specific classification of these products. In Canada, all herbal products are classified as natural health products and the safety and efficacy must be scientifically proven. In the USA, the development of botanical drugs is very recent. In particular, both herbal products classified as food supplements in Mexico and dietary supplements in the USA may have risks in both safety and efficacy.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.272
GPT teacher head0.411
Teacher spread0.138 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations32
Published2022
Admission routes1
Has abstractyes

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