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Record W4220701637 · doi:10.3390/ph15030359

Medical Cannabis in Pediatric Oncology: Friend or Foe?

2022· review· en· W4220701637 on OpenAlexafffund
Megan Malach, Igor Kovalchuk, Olga Kovalchuk

Bibliographic record

VenuePharmaceuticals · 2022
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Lethbridge
FundersMitacs
KeywordsCannabisMedicineNauseaVomitingPsychiatryCancerCannabinoidOncologyInternal medicine

Abstract

fetched live from OpenAlex

The antineoplastic effects of cannabis have been known since 1975. Since the identification of the components of the endogenous cannabinoid system (ECS) in the 1990s, research into the potential of cannabinoids as medicine has exploded, including in anti-cancer research. However, nearly all of this research has been on adults. Physicians and governing bodies remain cautious in recommending the use of cannabis in children, since the ECS develops early in life and data about cannabis exposure in utero show negative outcomes. However, there exist many published cases of use of cannabis in children to treat pediatric epilepsy and chemotherapy-induced nausea and vomiting (CINV) that show both the safety and efficacy of cannabis in pediatric populations. Additionally, promising preclinical evidence showing that cannabis has anti-cancer effects on pediatric cancer warrants further investigation of cannabis' use in pediatric cancer patients, as well as other populations of pediatric patients. This review aims to examine the evidence regarding the potential clinical utility of cannabis as an anti-cancer treatment in children by summarizing what is currently known about uses of medical cannabis in children, particularly regarding its anti-cancer potential.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0780.000

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 head0.185
GPT teacher head0.519
Teacher spread0.334 · 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.

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

Citations13
Published2022
Admission routes2
Has abstractyes

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