The Association Between Hospital High-volume Anesthesiology Care and Patient Outcomes for Complex Gastrointestinal Cancer Surgery
Bibliographic record
Abstract
OBJECTIVE: To examine the association of between hospital rates of high-volume anesthesiology care and of postoperative major morbidity. BACKGROUND: Individual anesthesiology volume has been associated with individual patient outcomes for complex gastrointestinal cancer surgery. However, whether hospital-level anesthesiology care, where changes can be made, influences the outcomes of patients cared at this hospital is unknown. METHODS: We conducted a population-based retrospective cohort study of adults undergoing esophagectomy, pancreatectomy, or hepatectomy for cancer from 2007 to 2018. The exposure was hospital-level adjusted rate of high-volume anesthesiology care. The outcome was hospital-level adjusted rate of 90-day major morbidity (Clavien-Dindo grade 3-5). Scatterplots visualized the relationship between each hospital's adjusted rates of high-volume anesthesiology and major morbidity. Analyses at the hospital-year level examined the association with multivariable Poisson regression. RESULTS: For 7893 patients at 17 hospitals, the rates of high-volume anesthesiology varied from 0% to 87.6%, and of major morbidity from 38.2% to 45.4%. The scatter plot revealed a weak inverse relationship between hospital rates of high-volume anesthesiology and of major morbidity (Pearson: -0.23). The adjusted hospital rate of high-volume anesthesiology was independently associated with the adjusted hospital rate of major morbidity (rate ratio: 0.96; 95% CI, 0.95-0.98; P <0.001 for each 10% increase in the high-volume rate). CONCLUSIONS: Hospitals that provided high-volume anesthesiology care to a higher proportion of patients were associated with lower rates of 90-day major morbidity. For each additional 10% patients receiving care by a high-volume anesthesiologist at a given hospital, there was an associated reduction of 4% in that hospital's rate of major morbidity.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".