Enhanced Recovery After Surgery (ERAS) for gastrointestinal surgery, part 2: consensus statement for anaesthesia practice
Bibliographic record
Abstract
BACKGROUND: The present interdisciplinary consensus review proposes clinical considerations and recommendations for anaesthetic practice in patients undergoing gastrointestinal surgery with an Enhanced Recovery after Surgery (ERAS) programme. METHODS: Studies were selected with particular attention being paid to meta-analyses, randomized controlled trials and large prospective cohort studies. For each item of the perioperative treatment pathway, available English-language literature was examined and reviewed. The group reached a consensus recommendation after critical appraisal of the literature. RESULTS: This consensus statement demonstrates that anaesthesiologists control several preoperative, intraoperative and postoperative ERAS elements. Further research is needed to verify the strength of these recommendations. CONCLUSIONS: Based on the evidence available for each element of perioperative care pathways, the Enhanced Recovery After Surgery (ERAS®) Society presents a comprehensive consensus review, clinical considerations and recommendations for anaesthesia care in patients undergoing gastrointestinal surgery within an ERAS programme. This unified protocol facilitates involvement of anaesthesiologists in the implementation of the ERAS programmes and allows for comparison between centres and it eventually might facilitate the design of multi-institutional prospective and adequately powered randomized trials.
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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 | 0.074 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".