Cannabinoid hyperemesis syndrome in North America: evaluation of health burden and treatment prevalence
Bibliographic record
Abstract
BACKGROUND: Cannabinoid hyperemesis syndrome (CHS) is a poorly understood vomiting disorder associated with chronic cannabis use. AIMS: To characterise patients experiencing CHS in North America and to obtain a population-based estimate of CHS treatment prevalence in Canada before and during the Covid-19 pandemic METHODS: Internet survey of 157 CHS sufferers in Canada and the United States. Administrative health databases for the province of Alberta (population 5 million) were accessed to measure emergency department (ED) visits for vomiting, with a concurrent diagnostic code for cannabis use. Three time periods of 1 year were assessed: prior to recreational cannabis legalisation (2017-2018), after recreational legalisation (2018-2019) and during the first year of the Covid-19 pandemic (2020-2021). RESULTS: Problematic cannabis use (defined as a CUDIT-R score ≥8) was universal among the survey cohort, and 59% and 68% screening for moderate or worse anxiety or depression, respectively. The overall treatment prevalence of CHS across all ages increased from 15 ED visits per 100,000 population (95% CI, 14-17) prior to legalisation, to 21 (95% CI, 20-23) after legalisation, to 32 (95% CI, 31-35) during the beginning of the Covid-19 pandemic (p < 0.001). Treatment prevalence among chronic cannabis users was as high as 6 per 1000 in the 16-24 age group. CONCLUSION: Survey data suggest patients with CHS almost universally suffer from a cannabis use disorder, which has significant treatment implications. Treatment prevalence in the ED has increased substantially over a very short time period, with the highest rates seen during the Covid-19 pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".