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Cannabinoids Used for Medical Purposes in Children and Adolescents

2024· review· en· W4402581434 on OpenAlexaff
Manik Chhabra, Mohamed Ben‐Eltriki, Holly Mansell, Mê‐Linh Lê, Richard J. Huntsman, Yaron Finkelstein, Lauren E. Kelly

Bibliographic record

VenueJAMA Pediatrics · 2024
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsChildren's Hospital Research Institute of ManitobaSickKids FoundationDalhousie UniversityUniversity of TorontoHospital for Sick ChildrenUniversity of SaskatchewanUniversity of Manitoba
Fundersnot available
KeywordsMedicineFamily medicineMEDLINEPediatricsMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

Importance: Cannabinoids are increasingly used for medical purposes in children. Evidence of the safety of cannabinoids in this context is sparse, creating a need for reliable information to close this knowledge gap. Objective: To study the adverse event profile of cannabinoids used for medical purposes in children and adolescents. Data Sources: For this systematic review and meta-analysis, MEDLINE, Embase, PsycINFO, and the Cochrane Library were searched for randomized clinical trials published from database inception to March 1, 2024, for subject terms and keywords focused on cannabis and children and adolescents. Search results were restricted to human studies in French or English. Study Selection: Two reviewers independently performed the title, abstract, and full-text review, data extraction, and quality assessment. Included studies enrolled at least 1 individual 18 years or younger, had a natural or pharmaceutical cannabinoid used as an intervention to manage any medical condition, and had an active comparator or placebo. Data Extraction and Synthesis: Two reviewers performed data extraction and quality assessment independently. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline and PRISMA-S guideline were used. Data were pooled using a random-effects model. Main Outcomes and Measures: The primary outcome was the incidence of withdrawals, withdrawals due to adverse events, overall adverse events, and serious adverse events in the cannabinoid and control arms. Secondary outcomes were the incidence of specific serious adverse events and adverse events based on organ system involvement. Results: Of 39 175 citations, 23 RCTs with 3612 participants were included (635 [17.6%] female and 2071 [57.3%] male; data not available from 2 trials); 11 trials (47.8%) included children and adolescents only, and the other 12 trials (52.2%) included children, adolescents, and adults. Interventions included purified cannabidiol (11 [47.8%]), nabilone (4 [17.4%]), tetrahydrocannabinol (3 [13.0%]), cannabis herbal extract (3 [13.0%]), and dexanabinol (2 [8.7%]). The most common indications were epilepsy (9 [39.1%]) and chemotherapy-induced nausea and vomiting (7 [30.4%]). Compared with the control, cannabinoids were associated with an overall increased risk of adverse events (risk ratio [RR], 1.09; 95% CI, 1.02-1.16; I2 = 54%; 12 trials), withdrawals due to adverse events (RR, 3.07; 95% CI, 1.73-5.43; I2 = 0%; 14 trials), and serious adverse events (RR, 1.81; 95% CI, 1.21-2.71; I2 = 59%; 11 trials). Cannabinoid-associated adverse events with higher RRs were diarrhea (RR, 1.82; 95% CI, 1.30-2.54; I2 = 35%; 10 trials), increased serum levels of aspartate aminotransferase (RR, 5.69; 95% CI, 1.74-18.64; I2 = 0%; 5 trials) and alanine aminotransferase (RR, 5.67; 95% CI, 2.23-14.39; I2 = 0%; 6 trials), and somnolence (RR, 2.28; 95% CI, 1.83-2.85; I2 = 8%; 14 trials). Conclusions and Relevance: In this systematic review and meta-analysis, cannabinoids used for medical purposes in children and adolescents in RCTs were associated with an increased risk of adverse events. The findings suggest that long-term safety studies, including those exploring cannabinoid-related drug interactions and tools that improve adverse event reporting, are needed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.356
Teacher spread0.335 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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