Children and Youth Who Use Cannabis for Pain Relief: Benefits, Risks, and Perceptions
Bibliographic record
Abstract
Julia J Woo,1 Emma A van Reekum,2 Tea Rosic,3 Zainab Samaan3 1Faculty of Medicine, University of Toronto, Toronto, ON, Canada; 2Faculty of Medicine, McMaster University, Hamilton, ON, Canada; 3Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, CanadaCorrespondence: Zainab SamaanSt. Joseph’s Healthcare Hamilton, West 5th Campus, 100 West 5th Street, Hamilton, ON L9C 0E3, CanadaTel +1-905-522-1155 x39215Email samaanz@mcmaster.caAbstract: We provide up-to-date perspectives on the benefits and risks of medical cannabis for pain management in children and youth. To date, only two studies (a case report and a small observational study) have examined the effects of medical cannabis on pain in children and youth. No controlled trial has commented on long-term safety of medical cannabis. Findings from the recreational cannabis literature reveal significant potential short- and long-term risks of regular cannabis use, including impaired driving, depression, suicidality, psychosis, and tolerance. Despite this, many children and youth are self-medicating with cannabis, and perceive regular cannabis use to be safe. There is a need for better education and counselling of patients regarding the benefits and risks of medical cannabis use.Keywords: marijuana, THC, CBD, adolescents, pediatrics
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".