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Record W2920287242 · doi:10.1213/ane.0000000000004018

American Society for Enhanced Recovery and Perioperative Quality Initiative Joint Consensus Statement on Perioperative Management of Patients on Preoperative Opioid Therapy

2019· review· en· W2920287242 on OpenAlexaff
David A. Edwards, Traci L. Hedrick, Jennifer Jayaram, Charles E. Argoff, Padma Gulur, Stefan D. Holubar, Tong J. Gan, Michael G. Mythen, Timothy E. Miller, Andrew Shaw, Julie K. Thacker, Matthew D. McEvoy

Bibliographic record

VenueAnesthesia & Analgesia · 2019
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of Alberta
FundersNational Institute for Health and Care Research
KeywordsMedicinePerioperativeIntensive care medicineMultidisciplinary approachHealth careOpioidPopulationMEDLINEPatient satisfactionMedical emergencyNursingAnesthesia

Abstract

fetched live from OpenAlex

Enhanced recovery pathways have quickly become part of the standard of care for patients undergoing elective surgery, especially in North America and Europe. One of the central tenets of this multidisciplinary approach is the use of multimodal analgesia with opioid-sparing and even opioid-free anesthesia and analgesia. However, the current state is a historically high use of opioids for both appropriate and inappropriate reasons, and patients with chronic opioid use before their surgery represent a common, often difficult-to-manage population for the enhanced recovery providers and health care team at large. Furthermore, limited evidence and few proven successful protocols exist to guide providers caring for these at-risk patients throughout their elective surgical experience. Therefore, the fourth Perioperative Quality Initiative brought together an international team of multidisciplinary experts, including anesthesiologists, nurse anesthetists, surgeons, pain specialists, neurologists, nurses, and other experts with the objective of providing consensus recommendations. Specifically, the goal of this consensus document is to minimize opioid-related complications by providing expert-based consensus recommendations that reflect the strength of the medical evidence regarding: (1) the definition, categorization, and risk stratification of patients receiving opioids before surgery; (2) optimal perioperative treatment strategies for patients receiving preoperative opioids; and (3) optimal discharge and continuity of care management practices for patients receiving opioids preoperatively. The overarching theme of this document is to provide health care providers with guidance to reduce potentially avoidable opioid-related complications including opioid dependence (both physical and behavioral), disability, and death. Enhanced recovery programs attempt to incorporate best practices into pathways of care. By presenting the available evidence for perioperative management of patients on opioids, this consensus panel hopes to encourage further development of pathways specific to this high-risk group to mitigate the often unintentional iatrogenic and untoward effects of opioids and to improve perioperative outcomes.

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 imitation

Not 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.

metaresearch head score (Codex)0.080
metaresearch head score (Gemma)0.119
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.119
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0050.004
Science and technology studies0.0030.002
Scholarly communication0.0060.003
Open science0.0070.008
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0050.004

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.

Opus teacher head0.095
GPT teacher head0.366
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations114
Published2019
Admission routes1
Has abstractyes

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