Current trends in anesthesia for esophagectomy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite marked improvements in perioperative outcomes, esophagectomy continues to be a high-risk operation associated with significant morbidity and mortality. Progress has been achieved through evidence-based changes in preoperative optimization, intraoperative ventilation strategies, fluid therapy, and analgesia, as well as expedited postoperative recovery pathways. This review will summarize the recent literature on the anesthetic management of patients undergoing esophageal resection. RECENT FINDINGS: The current focus in publications on the perioperative management of esophagectomy patients can be summarized under the umbrella term of enhanced recovery pathways, focusing on ventilation, fluid therapy, analgesia and minimally invasive surgical approaches. Lung protective ventilation reduces pulmonary complications in cases requiring one-lung ventilation. Excess fluid administration contributes to morbidity while restrictive approaches have not resulted in an increased risk of acute kidney injury. Goal-directed fluid therapy remains intuitive yet unproven. Thoracic epidural analgesia reduces the systemic inflammatory response, pulmonary complications, and enhances postoperative pain control, yet if causing perioperative hypotension may be associated with anastomotic leaks. Enhanced recovery pathways have facilitated low morbidity and mortality rates in a high-risk population but are heterogeneous and limited by a weak evidence base. Minimally invasive surgical approaches are increasingly popular and appear to have at least equivalent outcomes to open procedures. SUMMARY: The morbidity and mortality after esophagectomy remains high despite significant improvements over the last decades. Enhanced recovery pathways appear promising in achieving further marginal gains but at present are lacking large scale, prospective, multicenter evidence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".