High quantities: Evaluating the association between cannabis use and propofol anesthesia during endoscopy
Bibliographic record
Abstract
BACKGROUND: Endoscopy under propofol sedation has become a routine procedure. Given the number of Canadians undergoing an endoscopy annually, as well as the pervasive use of cannabis by many patients, understanding the effect of cannabis use on the propofol dose at endoscopy is highly relevant. We aimed to evaluate the association between cannabis exposure and the propofol dose needed to achieve adequate sedation at endoscopy. METHODS: A case-control study of individuals undergoing endoscopy was conducted at a single outpatient endoscopy clinic in London, Ontario between 2014 and 2017. Cases included all individuals with any self-reported cannabis exposure, while controls included all individuals without any self-reported history of cannabis use. Dose of propofol administered by a single anesthetist was collected on each subject as well as additional demographic and procedure-related covariates. RESULTS: Three hundred and eighteen participants were included (cases, n = 151; controls, n = 167). Cannabis exposure was associated with an increase in propofol dose (cases 0.33 mg/kg/minute ±0.24; controls, 0.18 mg/kg/minute ±0.11; p<0.0001). Cannabis exposure remained an independent predictor of propofol dose on multivariate linear regression accounting for other important covariates (p<0.0001). Daily cannabis users required a higher propofol dose than weekly or monthly users. Three procedural sedation-related complications occurred in the cannabis-exposed group, while none occurred in the unexposed group. CONCLUSION: Our data suggest that cannabis use is significantly associated with the quantity of propofol needed for sedation at endoscopy. Further study is needed to better understand the molecular basis for this possible drug-drug interaction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".