Acute pediatric cannabis intoxication: A scoping review
Bibliographic record
Abstract
Little is understood about the unintended consequences of cannabis liberalization on children. Subsequently, this scoping review aimed to map and identify evidence related to acute cannabis intoxication in children. We searched three medical literature databases from inception until October 2019. We identified 4644 information sources and included 158 which were mapped by topic area relating to 1) public health implications and considerations; 2) clinical management; and 3) experiences and information needs of HCPs and families. Public health implications were addressed by 129 (82%) and often reported an increased incidence of acute pediatric cannabis intoxications. Clinical information was reported in 116 (73%) and included information on signs and symptoms ( n = 106, 92%), clinical management processes ( n = 60, 52%), and treatment recommendations ( n = 42, 36%). Few sources addressed the experiences or information needs of either HCPs ( n = 5, <1%) treating children for acute cannabis intoxication or families ( n = 1, <1%) seeking care. Increasing incidence of acute cannabis intoxications concurrent with liberalization of cannabis legislation is clear, however, evidence around clinical management is limited. Additionally, further research exploring HCPs and families experiences and information needs around cannabis intoxication is warranted.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".