ASRA Pain Medicine consensus guidelines on the management of the perioperative patient on cannabis and cannabinoids
Bibliographic record
Abstract
BACKGROUND: The past two decades have seen an increase in cannabis use due to both regulatory changes and an interest in potential therapeutic effects of the substance, yet many aspects of the substance and their health implications remain controversial or unclear. METHODS: In November 2020, the American Society of Regional Anesthesia and Pain Medicine charged the Cannabis Working Group to develop guidelines for the perioperative use of cannabis. The Perioperative Use of Cannabis and Cannabinoids Guidelines Committee was charged with drafting responses to the nine key questions using a modified Delphi method with the overall goal of producing a document focused on the safe management of surgical patients using cannabinoids. A consensus recommendation required ≥75% agreement. RESULTS: Nine questions were selected, with 100% consensus achieved on third-round voting. Topics addressed included perioperative screening, postponement of elective surgery, concomitant use of opioid and cannabis perioperatively, implications for parturients, adjustment in anesthetic and analgesics intraoperatively, postoperative monitoring, cannabis use disorder, and postoperative concerns. Surgical patients using cannabinoids are at potential increased risk for negative perioperative outcomes. CONCLUSIONS: Specific clinical recommendations for perioperative management of cannabis and cannabinoids were successfully created.
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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 | 0.046 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".