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Record W2565136535 · doi:10.1097/won.0000000000000297

Executive Summary

2016· article· en· W2565136535 on OpenAlexafffundabout
Debbie Miller, Emily Pearsall, Debra Johnston, Monica Frecea, Marg McKenzie

Bibliographic record

VenueJournal of Wound Ostomy and Continence Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsUniversity Health NetworkProfessional Engineers OntarioSunnybrook Health Science CentreMount Sinai Hospital
FundersUniversity of Toronto
KeywordsGuidelineMedicineExecutive summaryColorectal surgeryMEDLINENursingGeneral surgerySurgeryAbdominal surgery

Abstract

fetched live from OpenAlex

Enhanced Recovery After Surgery (ERAS) is a multimodal program developed to decrease postoperative complications, improve patient safety and satisfaction, and promote early discharge. In the province of Ontario, Canada, a standardized approach to the care of adult patients undergoing elective colorectal surgery (including benign and malignant diseases) was adopted by 15 hospitals in March 2013. All colorectal surgery patients with or without an ostomy were included in the ERAS program targeting a length of stay of 3 days for colon surgery and 4 days for rectal surgery. To ensure the individual needs of patients requiring an ostomy in an ERAS program were being met, a Provincial ERAS Enterostomal Therapy Nurse Network was established. Our goal was to develop and implement an evidence-based, ostomy-specific best practice guideline addressing the preoperative, postoperative, and discharge phases of care. The guideline was developed over a 3-year period. It is based on existing literature, guidelines, and expert opinion. This article serves as an executive summary for this clinical resource; the full guideline is available as Supplemental Digital Content 1 (available at: http://links.lww.com/JWOCN/A36) to this executive summary.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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 designOther design
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

Citations28
Published2016
Admission routes3
Has abstractyes

Explore more

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