Perioperative Pain and Addiction Interdisciplinary Network (PAIN): protocol of a practice advisory for the perioperative management of buprenorphine using a modified Delphi process
Bibliographic record
Abstract
Introduction The ongoing opioid epidemic has necessitated increasing prescriptions of buprenorphine, which is an evidence-based treatment for opioid use disorder, and also shown to reduce harms associated with unsafe opioid administration. A systematic review of perioperative management strategies for patients taking buprenorphine concluded that there was little guidance for managing buprenorphine perioperatively. The aim of this project is to develop consensus guidelines on the optimal perioperative management strategies for this group of patients. In this paper, we present the design for a modified Delphi technique that will be used to gain consensus among patients and multidisciplinary experts in addiction, pain, community and perioperative medicine. Methods and analysis A national panel of experts identified by perioperative, pain and/or addiction systematic review authorship established an international profile in perioperative, pain and/or addiction research, community clinical excellence and by peer referral. A steering group will develop the first round with a list of indications to be rated by the panel of national experts, patients and allied healthcare professionals. In round 1, the expert panel will rate the appropriateness of each individual item and provide additional suggestions for revisions, additions or deletions. The definition of consensus will be seta priori. Consensus will be gauged for both appropriateness and inappropriateness of treatment strategies. Where an agreement is not reached and items are suggested for addition/deletion/modification, round 2 will take place over teleconference in order to obtain consensus. Ethics and dissemination Institutional research ethics board provided a waiver for this modified Delphi protocol. We plan on developing a national guideline for the management of patients taking buprenorphine in the perioperative period that will be generalisable across three sets of preoperative diagnoses including opioid use disorder and/or co-occurring pain disorders. The findings will be published in peer-reviewed publications and conference presentations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.202 | 0.164 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".