Cannabinoids for symptom management in children with cancer: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Despite the widespread use of medical cannabis, little is known regarding the safety, efficacy, and dosing of cannabis products in children with cancer. The objective of this study was to systematically appraise the existing published literature for the use of cannabis products in children with cancer. METHODS: This systematic review, registered with the International Prospective Register of Systematic Reviews (CRD42020187433), searched four databases: MEDLINE, Embase, PsycINFO, and the Cochrane Library. Abstracts and full texts were screened in duplicate. Data on types of cannabis products, doses, formulations, frequencies, routes of administration, indications, and clinical and demographic details as well as reported efficacy outcomes were extracted. Data on cannabinoid-related adverse events were also summarized. RESULTS: Out of 34,611 identified citations, 19 unique studies with a total of 1927 participants with cancer were included: eight retrospective chart reviews, seven randomized controlled trials, two open-label studies, and two case reports. The included studies reported the use of various cannabis products for the management of symptoms. Cannabinoids were commonly used for the management of chemotherapy-induced nausea and vomiting (11 of 19 [58%]). In controlled studies, somnolence, dizziness, dry mouth, and withdrawal due to adverse events were more commonly associated with the use of cannabinoids. Across all included studies, no serious cannabis-related adverse events were reported. CONCLUSIONS: Although there is evidence to support the use of cannabis for symptom management, in children with cancer, there is a lack of rigorous evidence to inform the dosing, safety, and efficacy of cannabinoids. Because of the increasing interest in using cannabis, there is an urgent need for more research on medical cannabis in children with cancer.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".