Outpatient surgery benchmarks and practice variation patterns: case controlled study
Bibliographic record
Abstract
BACKGROUND: Despite numerous potential benefits of outpatient surgery, there is currently a lack of national benchmarking data available for hospitals and surgeons to compare their own outcomes as they transition toward outpatient surgery. MATERIALS AND METHODS: Patients who underwent 14 common general surgery operations from 2016 to 2020 were identified in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Operations were selected based on frequency and the ability to be performed both inpatient and outpatient. Postoperative complications and readmissions were compared between patients who underwent inpatient vs outpatient surgery. After adjusting for patient comorbidities, multivariable models assessed the effect of patient characteristics on the odds of experiencing postoperative complications. A separate multi-institutional study of 21 affiliated hospitals assessed practice variation. RESULTS: In 13 of the 14 studied procedures, complications were lower for patients who were selected for outpatient surgery (all P <0.01); minimally invasive (MIS) adrenalectomy showed no difference ( P =0.61). Multivariable analysis confirmed these findings; the odds of experiencing any adverse events were lower following outpatient surgery in all operations but MIS adrenalectomy (OR 0.97; 95% CI: 0.47-2.02). Analysis of institutional practices demonstrated variation in the rate of outpatient surgery in certain breast, endocrine, and hernia repair operations. CONCLUSIONS: Institutional practice patterns may explain the national variation in the rate of outpatient surgery. While the present data does not support the adoption of outpatient surgery to less optimal candidates, addressing unexplained practice variations could result in improved utilization of outpatient surgery.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".