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Record W2943204414 · doi:10.1001/jamasurg.2019.1153

Guidelines for Perioperative Care in Cardiac Surgery

2019· review· en· W2943204414 on OpenAlexaff
Daniel T. Engelman, Walid Ben Ali, Judson B. Williams, Louis P. Perrault, V. Seenu Reddy, Rakesh C. Arora, Eric E. Roselli, Ali Khoynezhad, Marc Gerdisch, Jerrold H. Levy, Kevin W. Lobdell, Nick Fletcher, Matthias Kirsch, Gregg Nelson, Richard M. Engelman, Alexander J. Gregory, Edward M. Boyle

Bibliographic record

VenueJAMA Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of CalgaryUniversity of ManitobaSt. Boniface HospitalMontreal Heart Institute
Fundersnot available
KeywordsMedicinePerioperativeRandomized controlled trialProtocol (science)Intensive care medicineMEDLINECardiac surgeryEvidence-based medicineQuality of evidenceClinical trialBest evidenceSurgeryAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

Enhanced Recovery After Surgery (ERAS) evidence-based protocols for perioperative care can lead to improvements in clinical outcomes and cost savings. This article aims to present consensus recommendations for the optimal perioperative management of patients undergoing cardiac surgery. A review of meta-analyses, randomized clinical trials, large nonrandomized studies, and reviews was conducted for each protocol element. The quality of the evidence was graded and used to form consensus recommendations for each topic. Development of these recommendations was endorsed by the Enhanced Recovery After Surgery Society.

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)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0090.007
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0050.003
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0190.014

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 head0.188
GPT teacher head0.414
Teacher spread0.226 · 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 designSystematic review
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

Citations1,161
Published2019
Admission routes1
Has abstractyes

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