Toward standardization in esophageal cancer surgery: patterns of practice across high-volume European centers
Bibliographic record
Abstract
Esophageal cancer (EC) remains a leading cause of cancer-related mortality worldwide. For patients with locally advanced, non-metastatic EC, advances in perioperative care, and surgical techniques have led to improved outcomes; however, significant variation persists, and standardization remains limited. This study aimed to characterize current practice patterns among expert surgeons at high-volume European centers through a structured, in-depth survey. Eight expert upper gastrointestinal surgeons from European centers performing >60 esophagectomies annually participated in comprehensive interviews. Topics included preoperative care pathways for distal esophageal/gastroesophageal junction adenocarcinoma, technical aspects of Ivor Lewis esophagectomy, and postoperative recovery protocols. Additional focus areas included multidisciplinary team involvement, allied health integration, research program participation, and follow-up strategies. Widespread agreement (7-8 of 8 centers) was observed in several domains: national EC care regionalization, multidisciplinary cancer conference review of all patients, institutional EC research programs, use of prospective national/international databases, application of CROSS chemoradiotherapy for squamous cell carcinoma, and perioperative FLOT chemotherapy for adenocarcinoma. Common surgical techniques included minimally invasive Ivor Lewis esophagectomy, two-field lymphadenectomy with en-bloc thoracic duct ligation, nasogastric tube placement, omental wrap of the anastomosis, and Enhanced Recovery After Surgery-based postoperative protocols. The majority of centers (5-6/8) performed routine preoperative optimization (nutrition, smoking cessation, frailty screening, oral hygiene/microbiome assessment), jejunostomy placement, and postoperative contrast swallow studies. Areas with notable variability (≤4/8 centers) included intraoperative crural closure, pyloric drainage procedures, gastric conduit sizing, postoperative pain management, and follow-up imaging timelines. High-volume European centers demonstrated strong alignment in several programmatic and perioperative elements of EC care, particularly around enhanced recovery pathways and preoperative optimization. Nonetheless, key intraoperative and postoperative variations persist, highlighting opportunities for future research, consensus building, and standardization to improve patient outcomes.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".