Enhanced recovery pathways in pancreatic surgery: State of the art
Bibliographic record
Abstract
Pancreatic surgery is being offered to an increasing number of patients every year. Although postoperative outcomes have significantly improved in the last decades, even in high-volume centers patients still experience significant postoperative morbidity and full recovery after surgery takes longer than we think. In recent years, enhanced recovery pathways incorporating a large number of evidence-based perioperative interventions have proved to be beneficial in terms of improved postoperative outcomes, and accelerated patient recovery in the context of gastrointestinal, genitourinary and orthopedic surgery. The role of these pathways for pancreatic surgery is still unclear as high-quality randomized controlled trials are lacking. To date, non-randomized studies have shown that care pathways for pancreaticoduodenectomy and distal pancreatectomy are safe with no difference in postoperative morbidity, leading to early discharge and no increase in hospital readmissions. Hospital costs are reduced due to better organization of care and resource utilization. However, further research is needed to clarify the effect of enhanced recovery pathways on patient recovery and post-discharge outcomes following pancreatic resection. Future studies should be prospective and follow recent recommendations for the design and reporting of enhanced recovery pathways.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".