Overall Volume Trends in Esophageal Cancer Surgery Results From the Dutch Upper Gastrointestinal Cancer Audit
Bibliographic record
Abstract
OBJECTIVE: In the pursuit of quality improvement, this study aimed to investigate volume-outcome trends in oncologic esophagectomy in the Netherlands. SUMMARY OF BACKGROUND DATA: Concentration of Dutch esophageal cancer care was dictated by introducing an institutional minimum of 20 resections/yr. METHODS: This nationwide cohort study included all esophagectomy patients registered in the Dutch Upper Gastrointestinal Cancer Audit in 2016-2019 from hospitals currently still performing esophagectomies. Annual esophagectomy hospital volume was assigned to each patient and categorized into quartiles. Multivariable logistic regression investigated short-term surgical outcomes. Restricted cubic splines investigated if volume-outcome relationships eventually plateaued. RESULTS: In 16 hospitals, 3135 esophagectomies were performed. First volume quartile hospitals performed 24-39 resections/yr; second, third, and fourth quartile hospitals performed 40-53, 54-69, and 70-101, respectively. Compared to quartile 1, in quartiles 2 to 4, overall/severe/technical complication, anastomotic leakage, and prolonged hospital/intensive care unit stay rates were significantly lower and textbook outcome and lymph node yield were higher. When raising the cut-off from the first to second quartile, higher-volume centers had less technical complications [Adjusted odds ratio (aOR): 0.82, 95% confidence interval (CI): 0.70-0.96], less anastomotic leakage (aOR: 0.80, 95% CI: 0.66-0.97), more textbook outcome (aOR: 1.25, 95% CI: 1.07-1.46), shorter intensive care unit stay (aOR: 0.80, 95% CI: 0.69-0.93), and higher lymph node yield (aOR: 3.56, 95% CI: 2.68-4.77). For most outcomes the volume-outcome trend plateaued at 50-60 annual resections, but lymph node yield and anastomotic leakage continued to improve. CONCLUSION: Although this study does not reflect on individual hospital quality, there appears to be a volume trend towards better outcomes in high-volume centers. Projects have been initiated to improve national quality of care by reducing hospital variation (irrespective of volume) in outcomes in The Netherlands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".