Guidelines for Perioperative Care in Esophagectomy: Enhanced Recovery After Surgery (ERAS<sup>®</sup>) Society Recommendations
Bibliographic record
Abstract
INTRODUCTION: Enhanced recovery after surgery (ERAS) programs provide a format for multidisciplinary care and has been shown to predictably improve short term outcomes associated with surgical procedures. Esophagectomy has historically been associated with significant levels of morbidity and mortality and as a result routine application and audit of ERAS guidelines specifically designed for esophageal resection has significant potential to improve outcomes associated with this complex procedure. METHODS: A team of international experts in the surgical management of esophageal cancer was assembled and the existing literature was identified and reviewed prior to the production of the guidelines. Well established procedure specific components of ERAS were reviewed and updated with changes relevant to esophagectomy. Procedure specific, operative and technical sections were produced utilizing the best current level of evidence. All sections were rated regarding the level of evidence and overall recommendation according to the evaluation (GRADE) system. RESULTS: Thirty-nine sections were ultimately produced and assessed for quality of evidence and recommendations. Some sections were completely new to ERAS programs due to the fact that esophagectomy is the first guideline with a thoracic component to the procedure. CONCLUSIONS: The current ERAS society guidelines should be reviewed and applied in all centers looking to improve outcomes and quality associated with esophageal resection.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".