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Record W3097411329 · doi:10.3390/cancers12113203

Cannabidiol (CBD) as a Promising Anti-Cancer Drug

2020· review· en· W3097411329 on OpenAlexfundno aff
Emily S. Seltzer, Andrea Watters, Danny MacKenzie, Lauren M. Granat, Dong Zhang

Bibliographic record

VenueCancers · 2020
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersYork UniversityUniversity of Nebraska Medical Center
KeywordsCannabidiolDrugMedicinePharmacologyCancerCannabisPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Recently, cannabinoids, such as cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), have been the subject of intensive research and heavy scrutiny. Cannabinoids encompass a wide array of organic molecules, including those that are physiologically produced in humans, synthesized in laboratories, and extracted primarily from the Cannabis sativa plant. These organic molecules share similarities in their chemical structures as well as in their protein binding profiles. However, pronounced differences do exist in their mechanisms of action and clinical applications, which will be briefly compared and contrasted in this review. The mechanism of action of CBD and its potential applications in cancer therapy will be the major focus of this review article.

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 score0.007
Threshold uncertainty score0.022

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 science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.052
GPT teacher head0.398
Teacher spread0.346 · 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

Citations252
Published2020
Admission routes1
Has abstractyes

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