Which volume matters more? A systematic review and meta-analysis of hospital vs surgeon volume in intra-abdominal emergency surgery
Bibliographic record
Abstract
Background: Understanding the impact of hospital and surgeon volume on emergency intra-abdominal surgery procedures and determining which measure is more influential in improving outcomes can guide regionalization of care. This systematic review, meta-analysis, and meta-regression synthesizes evidence regarding the impact of hospital and surgeon volume on mortality.Methods: A literature search without language restriction was performed in the PubMed, Web of Science, and Cochrane databases. Cohort studies assessing the impact of hospital/surgeon volume on mortality after intra-abdominal emergency procedures with data collected after the year 2000 were included and analyzed using a random effects model. A sub-group analysis assessing impact of hospital volume on high and low complexity procedures and ruptured aortic artery aneurysm (RAAA) repair was performed. Another sub-group analysis comparing high-volume surgeons in a low-volume hospital and low-volume surgeons in a high-volume hospital was also performed. PROSPERO: CRD42022358879.Results: The search yielded 2153 articles, of which 33 cohort studies were included and determined to be good quality using the Newcastle Ottawa Scale. In 22 studies with available data for the meta-analysis, mortality was significantly higher in the low hospital volume and low surgeon volume cohort. The sub-group analysis found significantly higher mortality only in high complexity procedures and RAAA repair. Mortality was significantly lower in the cohort with high-volume surgeons at low-volume hospitals.Conclusion: High hospital volume was associated with lower mortality in all except low-complexity intra-abdominal emergency procedures. High surgeon volume was associated with lower mortality and there is limited evidence of it being the most protective.
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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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".