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Society for Ambulatory Anesthesia Guidelines for the Management of Postoperative Nausea and Vomiting

2007· article· en· W2131400892 on OpenAlexaff
Tong J. Gan, Tricia A. Meyer, Christian C. Apfel, Frances Chung, Peter J. Davis, Ashraf S. Habib, Vallire D. Hooper, Anthony L. Kovac, Peter Kranke, Paul S. Myles, Beverly K. Philip, Gregory P. Samsa, Daniel I. Sessler, James Temo, Martin R. Tramèr, Craig Vander Kolk, Mehernoor F. Watcha

Bibliographic record

VenueAnesthesia & Analgesia · 2007
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePostoperative nausea and vomitingAntiemeticAmbulatoryNauseaVomitingIntensive care medicineMultidisciplinary approachAnesthesiaSurgery

Abstract

fetched live from OpenAlex

In Brief The present guidelines were compiled by a multidisciplinary international panel of individuals with interest and expertise in postoperative nausea and vomiting (PONV) under the auspices of The Society of Ambulatory Anesthesia. The panel critically evaluated the current medical literature on PONV to provide an evidence-based reference tool for the management of adults and children who are undergoing surgery and are at increased risk for PONV. In brief, these guidelines identify risk factors for PONV in adults and children; recommend approaches for reducing baseline risks for PONV; identify the most effective antiemetic monotherapy and combination therapy regimens for PONV prophylaxis; recommend approaches for treatment of PONV when it occurs; and provide an algorithm for the management of individuals at increased risk for PONV. IMPLICATIONS: Evidence-based guidelines developed by an international multidisciplinary panel of experts and endorsed by Society for Ambulatory Anesthesia for the management of postoperative nausea and vomiting.

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)0.005
metaresearch head score (Gemma)0.018
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: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.003

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 head0.053
GPT teacher head0.344
Teacher spread0.291 · 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
GenreMethods

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

Citations715
Published2007
Admission routes1
Has abstractyes

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