Variation in Anesthesiology Provider–Volume for Complex Gastrointestinal Cancer Surgery
Bibliographic record
Abstract
OBJECTIVE: Examine between-hospital and between-anesthesiologist variation in anesthesiology provider-volume (PV) and delivery of high-volume anesthesiology care. BACKGROUND: Better outcomes for anesthesiologists with higher PV of complex gastrointestinal cancer surgery have been reported. The factors linking anesthesiology practice and organization to volume are unknown. METHODS: We identified patients undergoing elective esophagectomy, hepatectomy, and pancreatectomy using linked administrative health data sets (2007-2018). Anesthesiology PV was the annual number of procedures done by the primary anesthesiologist in the 2 years before the index surgery. High-volume anesthesiology was PV>6 procedures/year. Funnel plots to described variation in anesthesiology PV and delivery of high-volume care. Hierarchical regression models examined between-anesthesiologist and between-hospital variation in delivery of high-volume care use with variance partition coefficients (VPCs) and median odds ratios (MORs). RESULTS: Among 7893 patients cared for at 17 hospitals, funnel plots showed variation in anesthesiology PV (median ranging from 1.5, interquartile range: 1-2 to 11.5, interquartile range: 8-16) and delivery of HV care (ranging from 0% to 87%) across hospitals. After adjustment, 32% (VPC 0.32) and 16% (VPC: 0.16) of the variation were attributable to between-anesthesiologist and between-hospital differences, respectively. This translated to an anesthesiologist MOR of 4.81 (95% CI, 3.27-10.3) and hospital MOR of 3.04 (95% CI, 2.14-7.77). CONCLUSIONS: Substantial variation in anesthesiology PV and delivery of high-volume anesthesiology care existed across hospitals. The anesthesiologist and the hospital were key determinants of the variation in high-volume anesthesiology care delivery. This suggests that targeting anesthesiology structures of care could reduce variation and improve delivery of high-volume anesthesiology care.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".