Prevalence and association of non-medical cannabis use with post-procedural healthcare utilisation in patients undergoing surgery or interventional procedures: a retrospective cohort study
Bibliographic record
Abstract
Background: There is paucity of data regarding prevalence and key harms of non-medical cannabis use in surgical patients. We investigated whether cannabis use in patients undergoing surgery or interventional procedures patients was associated with a higher degree of post-procedural healthcare utilisation. Methods: 210,639 adults undergoing non-cardiac surgery between January 2008 and June 2020 at an academic healthcare network in Massachusetts, USA, were included. The primary exposure was use of cannabis, differentiated by reported ongoing non-medical use, self-identified during structured, preoperative nursing/physician interviews, or diagnosis of cannabis use disorder based on International Classification of Diseases, 9th/10th Revision, diagnostic codes. The main outcome measure was the requirement of advanced post-procedural healthcare utilisation (unplanned intensive care unit admission, hospital re-admission or non-home discharge). Findings: 16,211 patients (7.7%) were identified as cannabis users. The prevalence of cannabis use increased from 4.9% in 2008 to 14.3% by 2020 (p < 0.001). Patients who consumed cannabis had higher rates of psychiatric comorbidities (25.3 versus 16.8%; p < 0.001) and concomitant non-tobacco substance abuse (30.2 versus 7.0%; p < 0.001). Compared to non-users, patients with a diagnosis of cannabis use disorder had higher odds of requiring advanced post-procedural healthcare utilisation after adjusting for patient characteristics, concomitant substance use and socioeconomic factors (aOR [adjusted odds ratio] 1.16; 95% CI 1.02-1.32). By contrast, patients with ongoing non-medical cannabis use had lower odds of advanced post-procedural healthcare utilisation (aOR 0.87; 95% CI 0.81-0.92, compared to non-users). Interpretation: One in seven patients undergoing surgery or interventional procedures in 2020 reported cannabis consumption. Differential effects on post-procedural healthcare utilisation were observed between patients with non-medical cannabis use and cannabis use disorder. Funding: This work was supported by an unrestricted philantropic grant from Jeff and Judy Buzen to Maximilian S. Schaefer.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".