Evaluation of International Contemporary Operative Outcomes and Management Trends Associated With Esophagectomy: A 4-Year Study of >6000 Patients Using ECCG Definitions and the Online Esodata Database.
Bibliographic record
Abstract
OBJECTIVE: This study aims to verify the utility of international online datasets to benchmark and monitor treatment and outcomes in major oncologic procedures. BACKGROUND: The Esophageal Complication Consensus Group (ECCG) has standardized the reporting of complications after esophagectomy within the web-based Esodata.org database. This study will utilize the Esodata dataset to update contemporary outcomes and to monitor trends in practice in an era of rapid technical change. METHODS: This observational study, based on a prospectively developed specific database, updates esophagectomy outcomes collected between 2015 and 2018. Evolution in patient and operative demographics, treatment, complications, and quality outcome measures were compared between patients undergoing surgery in 2015 to 2016 and 2017 to 2018. RESULTS: Between 2015 and 2018, 6022 esophagectomies from 39 centers were entered into Esodata. Most patients were male (78.3%) with median age 63. Patients having minimally invasive esophagectomy constituted 3177 (52.8%), a chest anastomosis 3838 (63.7%), neoadjuvant chemoradiotherapy 2834 (48.7%), and R0 resections 5441 (93.5%). For quality measures, 30- and 90-day mortality was 2.0% and 4.5%, readmissions 9.7%, transfusions 12%, escalation in care 22.1%, and discharge home 89.4%. Trends in quality measures between 2015 and 2016 (2407 patients) and 2017 and 2018 (3318 patients) demonstrated significant (P < 0.05) improvements in readmissions 11.1% to 8.5%, blood transfusions 14.3% to 10.2%, and escalation in care from 24.5% to 20% A significantly (P < 0.05) reduced incidence in pneumonia (15.3%-12.8%) and renal failure (1.0%-0.4%) was observed. Anastomotic leak rates increased from 11.7% to 13.1%, whereas leaks requiring surgery decreased 3.3% and 3.0%, respectively. CONCLUSIONS: The Esodata database provides a valuable resource for assessing contemporary international outcomes. This study highlights an increased application of minimally invasive approaches, a high percentage of complications, improvements in pneumonia and key quality metrics, but with anastomotic leak rates still >10%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".