Perioperative pain and addiction interdisciplinary network (PAIN): protocol for the perioperative management of cannabis and cannabinoid-based medicines using a modified Delphi process
Bibliographic record
Abstract
INTRODUCTION: At the conception of this study (January 2019), a literature search by the authors found no evidence-based or consensus perioperative guidelines for patients consuming cannabis products, or for those patients in whom a cannabinoid medication could be considered for perioperative treatment. Currently, there is a large global population that consumes cannabis. The availability of cannabis has also increased this decade with greater legal access to cannabis products in some countries such as USA, Canada, Uruguay, Israel, Australia and Germany. There are recognised possible therapeutic benefits for the use of cannabis in patients with chronic pain, chronic neuropathic pain and chemotherapy-induced nausea and vomiting. There are also potential side effects from cannabis use such as psychosis, cannabis hyperemesis syndrome, misuse disorder and cannabis withdrawal syndrome. There is evidence that cannabis may also affect factors in the perioperative period such as monitoring, quality of analgesia, sleep and opioid consumption. Given the large population of persons using cannabis, the heterogeneity of cannabis products and the paucity (and heterogeneity) of perioperative literature surrounding it, perioperative guidelines for cannabis consuming patients are both lacking and necessary. In this paper, we present the design for a modified Delphi technique that has been started with the intent of deriving cannabis perioperative guidelines from the available medical literature and the consensus of multidisciplinary experts. MATERIALS, METHODS AND ANALYSIS: This study will use a scoping narrative literature review and modified Delphi process to generate cannabis perioperative guidelines. A scoping narrative review of cannabis in the perioperative period by the authors of this proposal was completed and provided to a panel of 17 experts. These experts were recruited for their knowledge and expertise regarding cannabis and/or perioperative medicine. They were asked to rate a series of indications and clinical scenarios in two rounds. During the first round, the expert panel was blinded to each other's participation. During the second round of this process, the expert panel met after being provided with an analysis of the first round's submissions so they could be discussed further and, if possible, reach a further consensus regarding them. Using the results obtained from the Delphi review process, a draft of proposed cannabis perioperative guidelines will be generated. These proposed guidelines will be returned to the expert panel for critiquing prior to their finalisation. ETHICS AND DISSEMINATION: Study and panellist data will be deidentified and stored as per institutional (Toronto General Hospital) guidelines. Institutional research ethics board provided a waiver for this modified Delphi protocol. Findings will be presented and published in peer-reviewed publications and conferences.
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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 | 0.102 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".