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Record W2943673605 · doi:10.1016/j.ajog.2019.04.028

International validation of Enhanced Recovery After Surgery Society guidelines on enhanced recovery for gynecologic surgery

2019· article· en· W2943673605 on OpenAlexaffabout
Lena Wijk, Ruzan Udumyan, Basile Pache, Alon D. Altman, Laura L. Williams, Kevin M. Elias, Jake McGee, Tiffany Wells, Leah Gramlich, Kevin Holcomb, Chahin Achtari, Olle Ljungqvist, Sean C. Dowdy, Gregg Nelson

Bibliographic record

VenueAmerican Journal of Obstetrics and Gynecology · 2019
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsRoyal Alexandra HospitalLondon Health Sciences CentreUniversity of ManitobaHealth Sciences Centre
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentMayo Clinic
KeywordsMedicinePerioperativeLaparotomyGynecologic oncologySurgeryAmerican society of anesthesiologistsGeneral surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Enhanced Recovery After Surgery Society publishes guidelines on perioperative care, but these guidelines should be validated prospectively. OBJECTIVE: To evaluate the association between compliance with Enhanced Recovery After Surgery Gynecologic/Oncology guideline elements and postoperative outcomes in an international cohort. STUDY DESIGN: The study comprised 2101 patients undergoing elective gynecologic/oncology surgery between January 2011 and November 2017 in 10 hospitals across Canada, the United States, and Europe. Patient demographics, surgical/anesthesia details, and Enhanced Recovery After Surgery protocol compliance elements (pre-, intra-, and postoperative phases) were entered into the Enhanced Recovery After Surgery Interactive Audit System. Surgical complexity was stratified according to the Aletti scoring system (low vs medium/high). The following covariates were accounted for in the analysis: age, body mass index, smoking status, presence of diabetes, American Society of Anesthesiologists class, International Federation of Gynecology and Obstetrics stage, preoperative chemotherapy, radiotherapy, operating time, surgical approach (open vs minimally invasive), intraoperative blood loss, hospital, and Enhanced Recovery After Surgery implementation status. The primary end points were primary hospital length of stay and complications. Negative binomial regression was used to model length of stay, and logistic regression to model complications, as a function of compliance score and covariates. RESULTS: Patient demographics included a median age 56 years, 35.5% obese, 15% smokers, and 26.7% American Society of Anesthesiologists Class III-IV. Final diagnosis was malignant in 49% of patients. Laparotomy was used in 75.9% of cases, and the remainder minimally invasive surgery. The majority of cases (86%) were of low complexity (Aletti score ≤3). In patients with ovarian cancer, 69.5% had a medium/high complexity surgery (Aletti score 4-11). Median length of stay was 2 days in the low- and 5 days in the medium/high-complexity group. Every unit increase in Enhanced Recovery After Surgery guideline score was associated with 8% (IRR, 0.92; 95% confidence interval, 0.90-0.95; P<.001) decrease in days in hospital among low-complexity, and 12% (IRR, 0.88; 95% confidence interval, 0.82-0.93; P<.001) decrease among patients with medium/high-complexity scores. For every unit increase in Enhanced Recovery After Surgery guideline score, the odds of total complications were estimated to be 12% lower (P<.05) among low-complexity patients. CONCLUSION: Audit of surgical practices demonstrates that improved compliance with Enhanced Recovery After Surgery Gynecologic/Oncology guidelines is associated with an improvement in clinical outcomes, including length of stay, highlighting the importance of Enhanced Recovery After Surgery implementation.

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)0.057
metaresearch head score (Gemma)0.121
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.057
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.121
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.290
Teacher spread0.265 · 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 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

Citations152
Published2019
Admission routes2
Has abstractyes

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