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Record W3014324912 · doi:10.1080/15384101.2020.1742952

Cannabinoids as anticancer therapeutic agents

2020· review· en· W3014324912 on OpenAlexafffund
Olga Kovalchuk, Igor Kovalchuk

Bibliographic record

VenueCell Cycle · 2020
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyAnticancer drugComputational biologyPharmacologyCancer researchDrug

Abstract

fetched live from OpenAlex

The recent announcement of marijuana legalization in Canada spiked many discussions about potential health benefits of Cannabis sativa. Cannabinoids are active chemical compounds produced by cannabis, and their numerous effects on the human body are primarily exerted through interactions with cannabinoid receptor types 1 (CB1) and 2 (CB2). Cannabinoids are broadly classified as endo-, phyto-, and synthetic cannabinoids. In this review, we will describe the activity of cannabinoids on the cellular level, comprehensively summarize the activity of all groups of cannabinoids on various cancers and propose several potential mechanisms of action of cannabinoids on cancer cells.

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)0.000
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.047
GPT teacher head0.380
Teacher spread0.333 · 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

Citations99
Published2020
Admission routes2
Has abstractyes

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