Using G-Computation to Estimate the Effect of Regionalization of Surgical Services on the Absolute Reduction in the Occurrence of Adverse Patient Outcomes
Bibliographic record
Abstract
BACKGROUND: Numerous studies have found that increased hospital or surgeon operative volumes, as measured by the number of procedures performed, are associated with improved patient outcomes after surgery. These findings have been used to support important health policy decisions about regionalization of surgical services, in which provision of specific surgical services is restricted to hospitals that maintain operative volumes above a specified threshold. The most common statistical approach in volume-outcome studies is to regress patient outcomes on a set of patient characteristics and a variable denoting provider volume. When outcomes are binary, such as operative mortality, logistic regression is used, resulting in the odds ratio being the reported measure of association. However, the odds ratio is a relative measure of effect and does not allow policy makers to estimate the absolute benefit of regionalization. OBJECTIVES: To describe how G-computation can be used to estimate the expected number of lives saved due to regionalization of surgical services. RESEARCH DESIGN: Retrospective cohort design of patients undergoing 1 of 3 different surgical procedures in Ontario, Canada. RESULTS: Regionalization of colorectal cancer surgery, esophagectomy, or pancreaticoduodenectomy in Ontario could reduce the average annual number of perioperative deaths by 20.2, 2.0, and 3.6, for the 3 procedures, respectively. CONCLUSIONS: The absolute reduction in number of operative deaths due to regionalization of surgical procedures can be calculated. This can help inform health policy debate about benefits of regionalization.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".