Guidelines for Perioperative Care for Pancreaticoduodenectomy: Enhanced Recovery After Surgery (ERAS<sup>®</sup>) Society Recommendations
Bibliographic record
Abstract
Abstract Background Protocols for enhanced recovery provide comprehensive and evidence‐based guidelines for best perioperative care. Protocol implementation may reduce complication rates and enhance functional recovery and, as a result of this, also reduce length‐of‐stay in hospital. There is no comprehensive framework available for pancreaticoduodenectomy. Methods An international working group constructed within the Enhanced Recovery After Surgery (ERAS ® ) Society constructed a comprehensive and evidence‐based framework for best perioperative care for pancreaticoduodenectomy patients. Data were retrieved from standard databases and personal archives. Evidence and recommendations were classified according to the GRADE system and reached through consensus in the group. The quality of evidence was rated “high”, “moderate”, “low” or “very low”. Recommendations were graded as “strong” or “weak”. Results Comprehensive guidelines are presented. Available evidence is summarised and recommendations given for 27 care items. The quality of evidence varies substantially and further research is needed for many issues to improve the strength of evidence and grade of recommendations. Conclusions The present evidence‐based guidelines provide the necessary platform upon which to base a unified protocol for perioperative care for pancreaticoduodenectomy. A unified protocol allows for comparison between centres and across national borders. It facilitates multi‐institutional prospective cohort registries and adequately powered randomised 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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".