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Record W2984775735 · doi:10.1017/cjn.2019.282

Position Statement on the Use of Medical Cannabis for the Treatment of Epilepsy in Canada

2019· review· en· W2984775735 on OpenAlexaffvenueabout
Juan Pablo Appendino, Cyrus Boelman, Paula Brna, Jorge G. Burneo, Curtis Claassen, Mary Connolly, Michael V T De Guzman, Paolo Federico, Deirdre Floyd, Richard J. Huntsman, Manouchehr Javidan, Nathalie Jetté, Laura Jurasek, Mark R. Keezer, Jonathan C. Lau, Bláthnaid McCoy, Richard S. McLachlan, Marcus Ng, Dang Khoa Nguyen, Aylin Y. Reid, Jong M. Rho, O. Carter Snead, José Francisco Téllez‐Zenteno, Laura Wang, Maria Zak

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2019
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsBC Children's HospitalWestern University
Fundersnot available
KeywordsPosition statementEpilepsyCannabisPosition (finance)Statement (logic)Medical cannabisMedicinePsychiatryBusinessPolitical scienceFamily medicineLaw

Abstract

fetched live from OpenAlex

In Canada, recreational use of cannabis was legalized in October 2018. This policy change along with recent publications evaluating the efficacy of cannabis for the medical treatment of epilepsy and media awareness about its use have increased the public interest about this agent. The Canadian League Against Epilepsy Medical Therapeutics Committee, along with a multidisciplinary group of experts and Canadian Epilepsy Alliance representatives, has developed a position statement about the use of medical cannabis for epilepsy. This article addresses the current Canadian legal framework, recent publications about its efficacy and safety profile, and our understanding of the clinical issues that should be considered when contemplating cannabis use for medical purposes.

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)0.003
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.550
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.153
GPT teacher head0.370
Teacher spread0.217 · 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
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

Citations11
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicCannabis and Cannabinoid Research→French-language works237,207→