Thirty‐day outcomes after gynecologic oncology surgery: A single‐center experience of enhanced recovery after surgery pathways
Bibliographic record
Abstract
INTRODUCTION: The purpose of the study is to evaluate the impact of an enhanced recovery after surgery (ERAS) program implemented in a Gynecologic Oncology population undergoing a laparotomy at a Canadian tertiary care center. MATERIAL AND METHODS: Prospectively collected data, using the American College of Surgeons' National Surgical Quality Improvement Program dataset (ACS NSQIP), was used to compare 30-day postoperative outcomes of gynecologic oncology patients undergoing a laparotomy before and after the 2018 implementation of an ERAS program in a Canadian regional cancer center. Patient demographics, surgical variables and postoperative outcomes of 187 patients undergoing surgery in 2019 were compared with those of 441 patients undergoing surgery between January 2016 and December 2017. Student's t, Mann-Whitney U and Chi-square tests, as well as multivariate linear and logistic regressions were used to evaluate baseline characteristics and 30-day postoperative complications. RESULTS: Length of stay was significantly shortened in the study population after introducing the ERAS protocol, from a mean of 4.7 (SD = 3.8) days to a mean of 3.8 (SD = 3.2) days (P = .0001). The overall complication rate decreased from 24.3% to 16% (P = .02). Significant decreases in the rates of postoperative infections (adjusted odds ratio [OR] 0.56, 95% confidence interval [CI] 0.31-0.99) and cardiovascular complications (adjusted OR 0.27, 95% CI 0.09-0.79) were noted, without a significant increase in readmission rate (adjusted OR 0.50, 95% CI 0.21-1.07). CONCLUSIONS: Introducing an ERAS program for gynecologic oncology patients undergoing laparotomy was effective in shortening length of stay and the overall complication rate without a significant increase in readmission. Advocacy for broader implementation of ERAS among gynecologic oncology services and ongoing discussion on challenges and opportunities in the implementation process are warranted to improve patient outcomes and experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".