Cannabinoids for Medical Purposes in Children: A Living Systematic Review
Bibliographic record
Abstract
AIM: We developed a living systematic review (LSR) that will continuously map the safety and reported benefit data related to cannabinoid use for medical purposes in children. METHODS: MEDLINE, Embase, PsycInfo, and the Cochrane Library were searched from inception to April 2023. Studies involving at least one child < 18 years who was administered plant-derived or pharmaceutical cannabinoids as an intervention or treatment for medical conditions were included. RESULTS: Of 37 189 identified citations, 276 studies were included: 84 interventional, 131 observational, 54 surveys, and 7 qualitative studies. Among interventional and observational studies, common indications for cannabinoids in children were refractory epilepsy (n = 146 studies, 188 726 participants), cancer and cancer symptoms (n = 30 studies, 208 753 participants), and autism spectrum disorder (n = 18 studies, 1285 participants). Common cannabinoids identified in interventional studies were purified cannabidiol (CBD) (78.6%, n = 66 studies, 5235 participants) with dose range of 2-50 mg/kg/day, tetrahydrocannabinol (6%, n = 5 studies, 148 participants) with dose range of 2.5-10 mg/day (max dose of tetrahydrocannabinol in nabiximols 32.4 mg) and nabilone (6%, n = 5 studies, 267 participants) with dose range of 0.5-2 mg/day. In randomised controlled trials, purified cannabidiol was reported to reduce seizure frequency ranging between 30% and 50%. Common adverse events (> 20% studies) in studies enrolling children were somnolence, diarrhoea, vomiting, and decreased appetite. CONCLUSION: These findings will continue to be updated to inform practice and reveal knowledge gaps for future research.
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 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 | 0.027 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".