Consensus guidelines for enhanced recovery after gastrectomy
Bibliographic record
Abstract
BACKGROUND: Application of evidence-based perioperative care protocols reduces complication rates, accelerates recovery and shortens hospital stay. Presently, there are no comprehensive guidelines for perioperative care for gastrectomy. METHODS: An international working group within the Enhanced Recovery After Surgery (ERAS®) Society assembled an evidence-based comprehensive framework for optimal perioperative care for patients undergoing gastrectomy. Data were retrieved from standard databases and personal archives. Evidence and recommendations were classified according to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system and were discussed until consensus was reached within the group. The quality of evidence was rated 'high', 'moderate', 'low' or 'very low'. Recommendations were graded as 'strong' or 'weak'. RESULTS: The available evidence has been summarized and recommendations are given for 25 items, eight of which contain procedure-specific evidence. The quality of evidence varies substantially and further research is needed for many issues to improve the strength of evidence and grade of recommendations. CONCLUSION: The present evidence-based framework provides comprehensive advice on optimal perioperative care for the patient undergoing gastrectomy and facilitates multi-institutional prospective cohort registries and adequately powered randomized trials for further research.
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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 | 0.070 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".