A Systematic Review on Cannabis Hyperemesis Syndrome and Its Management Options
Bibliographic record
Abstract
INTRODUCTION: Several forms of cannabinoids are currently being used to manage nausea and vomiting (N/V). Emerging cases of refractory N/V associated with chronic cannabis use among adults and older patients have been reported named cannabis hyperemesis syndrome (CHS). CHS is a condition that leads to repeated and severe N/V in long-term users of cannabinoids. OBJECTIVE: The aim of this study was to outline current treatments for the management of CHS. METHODS: A systematic review was conducted using PubMed, Ovid MEDLINE, Cochrane Central, EMBASE, and Google Scholar. Databases were used to search for articles on CHS published from January 2009 to June 2021, yielding 225 results of which 17 were deemed relevant and underwent review by 2 separate reviewers. RESULTS: The duration of cannabis administration ranged between 6 months to 11 years may precipitate symptoms of CHS. The Rome IV diagnostic criteria of CHS require cannabinoid use and persistence of N/V symptoms for at least the past 6 months. Cannabis cessation is noted to be the most successful management, but other treatments also demonstrated symptom relief; these include hot water hydrotherapy, topical capsaicin cream, haloperidol, droperidol, benzodiazepines, propranolol, and aprepitant administration. CONCLUSION: More research on CHS is needed to enhance knowledge translation, education, and create awareness in the medical community on the side effects of cannabinoids and to propose the best treatment options.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".