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Record W4386834031 · doi:10.3917/pinc.003.0002

The regulation of cannabis-based medicinal products across jurisdictions

2020· article· en· W4386834031 on OpenAlexaboutno aff
Olivier Reisch, David Alexandre, Michèle Büchler

Bibliographic record

VenuePin Code · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisLegislationPolitical scienceBusinessLawMedicinePsychiatry

Abstract

fetched live from OpenAlex

Cannabis-based medicinal products are often referred to as “cannabis-based products for medicinal use in humans” and will, for the purposes of this article, be referred to as CBPMs hereafter. CBPMs are today authorized to be administered as a medicine in many countries, including Luxembourg. In view of the above, we consider the current state of the legislation and distribution of CBPMs across multiple jurisdictions around the world, such as Australia, Austria, Canada, Denmark, Germany, Italy, Luxembourg, the Netherlands, Poland, Spain and the United Kingdom. The law referred to in this article is accurate as at 28 January 2020. The legal framework relating to the regulation of CBPMs is under regular review in a number of jurisdictions and therefore subject to change. The contents of this article do not contain, nor consist in, any legal advice.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.030
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0050.008
Scholarly communication0.0080.004
Open science0.0030.005
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0090.003

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 head0.035
GPT teacher head0.337
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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