Baseline Cannabinoid Use Is Associated with Increased Sedation Requirements for Outpatient Endoscopy
Bibliographic record
Abstract
Background and Aims: Given the underlying properties of cannabinoids, we aimed to assess associations between cannabinoid use and sedation requirements for esophagogastroduodenoscopy (EGD) and colonoscopy. Methods: A prospective cohort study was conducted at three endoscopy units. Adult outpatients undergoing EGD or colonoscopy with endoscopist-directed conscious sedation (EDCS) were given questionnaires on cannabinoid use and relevant parameters. Outcomes included intraprocedural midazolam, fentanyl, and diphenhydramine use, procedural tolerability, and adverse events. Multivariable logistic regression was performed to yield adjusted odds ratios (AORs) of outcomes. Results: A total of 419 patients were included. Baseline cannabinoid use was associated with high midazolam use, defined as ≥5 mg, during EGD (AOR 2.89, 95% confidence interval, CI: 1.19–7.50), but not during colonoscopy (AOR 0.89, 95% CI 0.41–1.91). Baseline cannabinoid use was associated with the administration of any diphenhydramine during EGD (AOR 3.04, 95% CI: 1.29–7.30) with a similar nonsignificant trend for colonoscopy (AOR 2.36, 95% CI: 0.81–7.04). Baseline cannabinoid use was associated with increased odds of requiring high total sedation, defined as any of midazolam ≥5 mg, fentanyl ≥100 mcg, or any diphenhydramine during EGD (AOR 3.72, 95% CI: 1.35–11.68). Cannabinoid use was not independently associated with fentanyl use, intraprocedural awareness, discomfort, or adverse events. Conclusions: Baseline cannabinoid use was associated with higher sedation use during endoscopy with EDCS, particularly with midazolam and diphenhydramine. Given increasingly widespread cannabinoid use, endoscopists should be equipped with optimal sedation strategies for this population. As part of the informed consent process, cannabis users should be counseled that they may require higher sedation doses to achieve the same effect.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".