042. OPTIMAL HOSPITAL VOLUME TO MINIMISE MORTALITY AFTER ESOPHAGECTOMY FOR CANCER IN LOW POPULATION DENSITY COUNTRIES: IN AUSTRALIA AND NEW ZEALAND
Bibliographic record
Abstract
Abstract Background A relationship between hospital volume and postoperative mortality following esophagectomy for cancer has been reported in the UK and Europe, leading to centralisation of surgery for esophageal cancer in most of these countries, including the Netherlands. It is unclear if this is replicated in countries with low population density such as Australia and New Zealand (ANZ). This study determined the relationship between hospital volume and mortality following esophagectomy in ANZ to define optimal hospital caseload. Methods As the standard of care following esophagectomy in ANZ is admission to an intensive care unit (ICU), the prospective ANZ Intensive Care Society Adult Patient Database was used to identify patients undergoing esophagectomy from 2005–22. In-hospital mortality was first determined for hospitals with annual caseloads defined as high (18+), medium-high (12–17), medium-low (6–11) and low (1–5). To define optimal caseload, mortality was also analysed against hospital volume using piecewise linear regression and non-linear (restricted cubic spline) methods. Results 6234 patients underwent esophagectomy in 161 hospitals, 25% of procedures were performed in low-volume hospitals (n = 1558), and 19.9% in high-volume hospitals (n = 1239). Overall, in-hospital mortality ranged from 0.73% in the high-volume hospitals to 5.71% in the low-volume hospitals. High-volume hospitals also had a shorter length of stay in hospital (p < 0.001) and ICU (p < 0.001). The optimal annual hospital volume for the lowest mortality was identified as 21 cases/year. After adjusting for confounders in multivariable analysis, low-volume hospitals showed the highest risk of mortality with ORs of 3.98 (low), 3.39 (medium-low), 3.32 (medium-high) vs high-volume (all p < 0.05). Conclusion A positive volume-outcome relationship in ANZ was demonstrated for mortality following esophagectomy, with hospitals performing 21 or more surgeries per year delivering lowest mortality, which is similar to the previous European studies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".