Intraoperative pharmacologic opioid minimisation strategies and patient-centred outcomes after surgery: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: For close to a century opioid administration has been a standard of care to complement anaesthesia during surgery. Considering the worldwide opioid epidemic, this practice is now being challenged and there is a growing use of systemic pharmacological opioid minimising strategies. Our aim is to conduct a scoping review that will examine clinical trials that have evaluated the impact of intraoperative opioid minimisation strategies on patient-centred outcomes and identify promising strategies. METHODS AND ANALYSIS: Our scoping review will follow the framework developed by Arksey and O'Malley. We will search MEDLINE, Embase, CENTRAL, Web of Science and CINAHL from their inception approximately in March 2023. We will include randomised controlled trials, assessing the impact of systemic intraoperative pharmacologic opioid minimisation strategies on patient-centred outcomes. We define an opioid minimisation strategy as any non-opioid drug with antinociceptive properties administered during the intraoperative period. Patient-centred outcomes will be defined and classified based on the consensus definitions established by the Standardised Endpoints in Perioperative Medicine initiative (StEP-COMPAC group) and informed by knowledge users and patient partners. We will use a coproduction approach involving interested parties. Our multidisciplinary team includes knowledge users, patient partners, methodologists and knowledge user organisations. Knowledge users will provide input on methods, outcomes, clinical significance of findings, implementation and feasibility. Patient partners will participate in assessing the relevance of our design, methods and outcomes and help to facilitate evidence translation. We will provide a thorough description of available clinical trials, compare their reported patient-centred outcome measures with established recommendations and identify promising strategies. ETHICS AND DISSEMINATION: Ethics approval is not required for the review. Our scoping review will inform future research including clinical trials and systematic reviews through identification of important intraoperative interventions. Results will be disseminated through a peer-reviewed publication, presentation at conferences and through our network of knowledge user collaborators. REGISTRATION: Open Science Foundation (currently embargoed).
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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 | 0.092 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".