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Record W3010469697 · doi:10.1503/cjs.006819

Development of a clinical pathway for enhanced recovery in colorectal surgery: a Canadian collaboration

2020· article· en· W3010469697 on OpenAlexafffundvenueabout
Leah Gramlich, Brae Surgeoner, Gabriele Baldini, Erin Ballah, Melinda Baum, Franco Carli, Ahmer Karimuddin, Gregg Nelson, Philippe Richebé, Deborah Watson, Carla Williams, Claude Laflamme

Bibliographic record

VenueCanadian Journal of Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsCanadian Patient Safety InstituteHôpital Maisonneuve-RosemontMcGill UniversityUniversity of CalgaryUniversité de MontréalUniversity of AlbertaMinistry of HealthUniversity of British ColumbiaAlberta Health Services
FundersCanadian Patient Safety Institute
KeywordsMedicineMultidisciplinary approachColorectal surgeryPatient safetyClinical pathwayGeneral surgeryMedical emergencyHealth careNursingIntensive care medicineSurgeryAbdominal surgery

Abstract

fetched live from OpenAlex

Summary: Enhanced Recovery After Surgery (ERAS) is a model of care that was introduced in the late 1990s by a group of surgeons in Europe. The model consists of a number of evidence-based principles that support better outcomes for surgical patients, including improved patient experience, reduced length of stay in hospital, decreased complication rates and fewer hospital readmissions. A number of Canadian surgical care teams have already adopted ERAS principles and have reported positive outcomes. Arising from the Canadian Patient Safety Institute’s Integrated Patient Safety Action Plan for Surgical Care Safety, and with support from numerous partner organizations from across the country, Enhanced Recovery Canada is leading the drive to improve surgical safety across the country and help disseminate these ERAS principles. We discuss the development of a multidisciplinary clinical pathway for elective colorectal surgery to help guide Canadian clinicians.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.305
Teacher spread0.224 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2020
Admission routes4
Has abstractyes

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