Hospital volume and hospital mortality for esophagectomy
Bibliographic record
Abstract
BACKGROUND: Hospital mortality after esophagectomy has decreased from 29% to 7.5% over the last decades because of improved surgical techniques and better perioperative care. Suggestions have been made that a further decrease in hospital mortality may be achieved by centralization of esophagectomies in high volume centers. METHODS: The effect of hospital volume on hospital mortality after esophagectomy in the Netherlands was analyzed based on data from the Dutch National Medical Registry and the Dutch Network and National Database for Pathology over the period 1993-1998. RESULTS: Annually, approximately 310 (range, 264-321) esophagectomies are performed in the Netherlands. Fifty-two percent are performed in 43-55 low volume centers (1-10 resections a year). Six percent are performed in 1-3 medium volume centers (11-20 resections a year). The remainder (42%) is performed in two high volume centers (> 50 resections a year). Hospital mortality is 12.1%, 7.5% and 4.9% respectively (P < 0.001). The high volume centers seem to see slightly more advanced tumors than the low and medium volume centers. CONCLUSIONS: There is a significant (inverse) relation between hospital mortality and hospital volume for esophageal resection in the Netherlands. Although hospital mortality is not the only measure for quality of care, these data suggest a potential beneficial effect to centralization of esophagectomy in the Netherlands.
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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 | 0.001 | 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) | 0.002 | 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".