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Record W4393992924 · doi:10.1093/pch/pxad078

Medical cannabis for children: Evidence and recommendations

2024· review· en· W4393992924 on OpenAlexafffundabout
Lauren E. Kelly, Michael Rieder, Yaron Finkelstein

Bibliographic record

VenuePaediatrics & Child Health · 2024
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsCanadian Paediatric Society
FundersIWK Health CentreMcMaster UniversityUniversité Laval
KeywordsCannabisCannabidiolTolerabilityMedicineMedical cannabisDosingDravet syndromePsychiatryTetrahydrocannabinolDrugClinical PracticeFamily medicineAdverse effectCannabinoidPharmacologyEpilepsyInternal medicine

Abstract

fetched live from OpenAlex

Interest in using cannabis products for a medical purpose in children under the age of 18 years is increasing. There are many medical cannabis products available that can include cannabidiol (CBD) or delta-9-tetrahydrocannabinol (THC), or both. Despite many therapeutic claims, there are few rigorous studies to inform the dosing, safety, and efficacy of medical cannabis in paediatric clinical practice. This statement reviews the current evidence and provides recommendations for using medical cannabis in children. Longer-term (2-year) reports support the sustained tolerability and efficacy of cannabidiol therapy for patients with Lennox-Gastaut and Dravet syndromes. CBD-enriched cannabis extracts containing small amounts of THC have been evaluated in a small number of paediatric patients, and further research is needed to inform clinical practice guidelines. Given the widespread use of medical cannabis in Canada, paediatricians should be prepared to engage in open, ongoing discussions with families about its potential benefits and risks, and develop individualized plans that monitor efficacy, reduce harms, and mitigate drug-drug interactions.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.054
GPT teacher head0.423
Teacher spread0.368 · 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 designSystematic review
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

Citations16
Published2024
Admission routes3
Has abstractyes

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