Outcomes of Enhanced Recovery after Surgery (ERAS) in Gynecologic Oncology: A Review
Bibliographic record
Abstract
Enhanced Recovery After Surgery (ERAS) is a global surgical quality improvement program that started in colorectal surgery and has now expanded to numerous specialties, including gynecologic oncology. ERAS guidelines comprise multidisciplinary, evidence-based recommendations in the preoperative, intraoperative, and postoperative period; these interventions broadly encompass patient education, anesthetic choice, multimodal pain control, avoidance of unnecessary drains, maintenance of nutrition, and prevention of emesis. Implementation of ERAS has been shown to be associated with improved clinical outcomes (length of hospital stay, complications, readmissions) and cost. Marx and colleagues first demonstrated the feasibility of ERAS in gynecologic oncology in 2003; since then, over 30 comparative studies and 4 guidelines have been published encompassing major gynecologic surgery, cytoreductive surgery, and vulvar/vaginal surgery. Implementation of ERAS in gynecologic oncology has been demonstrated to provide improvements in length of stay, complications, cost, opioid use, and patient satisfaction. Increased compliance with ERAS guidelines has been associated with greater improvement in outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".