Trends in surgical techniques for the treatment of esophageal and gastroesophageal junction cancer: the 2022 update
Bibliographic record
Abstract
The aim of this study was to evaluate the current practice in surgical techniques for esophageal and gastroesophageal junction cancer surgery worldwide and to compare the results to the previous surveys in 2007 and 2014. An online survey was sent out among surgical members of the International Society for Diseases of the Esophagus, the World Organization for Specialized Studies on Disease of the Esophagus, the International Gastric Cancer Association, the Association of Upper Gastrointestinal Surgery of Great Britain and Ireland and Dutch gastroesophageal surgeons via the network of the investigators. In total, 260 surgeons completed the survey representing 52 countries and 6 continents; Europe 56%, Oceania 14%, Asia 14%, South-America 9%, North-America 7%. Of the responding surgeons, 39% worked in a hospital that performed >51 esophagectomies per year. Total minimally invasive esophagectomy was the preferred technique (53%) followed by hybrid esophagectomy (26%) of which 7% consisted of a minimally invasive thoracic phase and 19% of a minimally invasive abdominal phase. Total open esophagectomy was preferred by 21% of the respondents. Total minimally invasive esophagectomy was significantly more often performed in high-volume centers compared with non-high-volume centers (P = 0.002). Robotic assistance was used in 13% during the thoracic phase and 6% during the abdominal phase. Minimally invasive transthoracic esophagectomy has become the preferred approach for esophagectomy. Although 21% of the surgeons prefer an open approach, 26% of the surgeons perform a hybrid procedure which may reflect further transition towards the use of total minimally invasive esophagectomy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".