Mental health adverse events with cannabis use diagnosed in the Emergency Department: what are we finding now and are our findings accurate?
Bibliographic record
Abstract
We have previously reviewed the types and numbers of cannabis-associated adverse events that have mental health presentations that are encountered in the Emergency Department. A particular challenge in examining these events is disentangling cannabis use adverse events from adverse events associated with use of multiple recreational substances. Since that review was published, cannabis legalization for recreational use has greatly expanded world-wide and with these changes in the legal climate has come clearer information around the frequency of adverse events seen in the Emergency Department. However, as we examined the current state of the literature, we also examined some of research designs and the biases that may be impacting the validity of the data in this field. The biases both of clinicians and researchers as well as research approaches to studying these events may be impacting our ability to assess the interaction between cannabis and mental health. For example, many of the studies performed examining cannabis-related admissions to the Emergency Department were administrative studies that relied on front line clinicians to identify and attribute that cannabis use was associated with any particular admission. This narrative review provides an overview on what we currently know about mental health adverse events in the Emergency Department with a focus on the mental health impacts both for those with and without a history of mental illness. The evidence that cannabis use can adversely impact genders and sexes differently is also discussed. This review outlines what the most common adverse events related to mental health with cannabis use are; as well as noting the most concerning but much rarer events that have been reported. Additionally, this review suggests a framework for critical evaluation of this field of study going forward.
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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 | 0.014 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".