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Record W2185203593 · doi:10.25258/ijpqa.4.4.2

A Comparative Study of Regulatory Registration Procedure of Nutraceuticals in India, Canada and Australia

2013· article· en· W2185203593 on OpenAlexaboutno aff
Avinash Sharma, Pramod Kumar, Pankaj Kumar Sharma, Birendra Shrivastav

Bibliographic record

VenueInternational Journal of Pharmaceutical Quality Assurance · 2013
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsnot available
Fundersnot available
KeywordsNutraceuticalDietary supplementProduct (mathematics)IngredientBusinessHealth benefitsMedicineBiotechnologyTraditional medicineFood scienceBiology

Abstract

fetched live from OpenAlex

“Nutraceuticals” are the combination of Nutrition and pharmaceutical. The term Nutraceutical was given by Dr. Stephen in 1989. A dietary supplement is a product taken by mouth that contains a dietary ingredient (Vitamins, Minerals, Herbs, Amino acids etc.) Numerous definitions and nomenclature for dietary supplements exist worldwide. In India Food Safety and Standards authority (FSSA), defines Nutraceuticals as “foods for special dietary uses or functional foods or health supplements”. In Canada Nutraceuticals are known as Natural health Products. In Australia, traditional, herbal, natural and alternative medicines and remedies are referred to as ‘complementary medicines’. Every country has their own guidelines, regulatory requirements which deal with regulatory registration procedures of Nutraceuticals. In order to enter the Indian nutraceutical market, some of the very important areas of focus include product evaluation, actual product analysis, procuring licenses and developing India specific health and label claims. In Canada Product licensing, Site licensing, GMP, Adverse drug reporting, Clinical trials and Health claims. And in Australia Product licensing, GMP, Site licensing, Labelling and Helath claims are required.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.426
Teacher spread0.358 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2013
Admission routes1
Has abstractyes

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