The impact of surgical volume on perioperative safety after urethroplasty: a population-based study
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to assess whether the risk of perioperative complications after urethroplasty was affected by hospital annual surgical volume (ASV). METHODS: In the Nationwide Inpatient Sample, we searched for patients who underwent urethroplasty between 2001 and 2015. Hospitals were categorized into empirically determined tertiles, according to ASV of performed urethroplasties and divided into low (<3) (LVC), intermediate (3-19) (IVC) and high (>20) volume centers (HVC). Multivariable logistic regression (MLR) analyses examined the effect of ASV on perioperative complications and on four specific sub-types of post-operative complications. RESULTS: A weighted estimate of 39 912 patients underwent urethroplasty in the US. 34.9% were operated in HVC, while the rate of performed urethroplasties increased in LVC and decreased in HVC. Overall, 1.1%, 18.8% and 2.1% patients respectively experienced intraoperative, post-operative, and transfusions complications. At MLR, IVC and LVC were associated with higher risk of both intraoperative (IVC: OR 2.65, P=0.0008; LVC: OR 4.98, P<0.0001), post-operative (IVC: OR 1.14, P=0.01; LVC: OR 1.26, P=0.001) and transfusions complications (IVC: OR 1.85, P<0.001; LVC: OR 3.03, P=0.01). LVC was also associated with higher risk of hematuria (OR 3.77), urinary infections (OR 1.60) and sepsis (OR 2.83) complications. CONCLUSIONS: Approximately 65% of patients were operated in IVC and LVC, and patients treated in IVC or LVC had higher risk of developing both intra and post-operative complications. These data provide important indicators for policy makers to categorize institution based on urethroplasty outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".