The Effect of Surgeon Volume on Outcomes and Resource Use for Vaginal Hysterectomy
Bibliographic record
Abstract
OBJECTIVE: To estimate the effect of surgical volume on outcomes and resource use in women undergoing vaginal hysterectomy. METHODS: Women who underwent total vaginal hysterectomy and were registered in the Perspective database were examined. Perspective is a nationwide database developed to measure quality and resource use. Procedure-associated intraoperative, perioperative, and postoperative medical complications as well as hospital readmission, length of stay, intensive care unit (ICU) use, operating time, and cost were analyzed. Based on the overall gynecologic surgical volume and vaginal surgical volume of their surgeons, patients were stratified into tertiles. Complications were compared using adjusted generalized estimating equations and reported as odds ratios (ORs). RESULTS: A total of 77,109 patients operated on by 6,195 gynecologic surgeons were identified. After adjustment for the effects of other demographic variables and concomitant procedures, patients operated on by high-volume vaginal surgeons were 31% (OR 0.69; 95% confidence interval [CI] 0.59-0.80) less likely to experience an operative injury, whereas perioperative complications were reduced by 19% (OR 0.81; 95% CI 0.72-0.92), medical complications decreased by 24% (OR 0.76; 95% CI 0.67-0.86), ICU admission reduced by 46% (OR 0.56; 95% CI 0.43-0.73), and the transfusion rate decreased by 28% (OR 0.72; 95% CI 0.61-0.85) in patients treated by high-volume vaginal surgeons, whereas rates of readmission were higher (OR 1.24; 95% CI 1.04-1.47) in patients treated by high-volume surgeons. Operative times were lower in patients operated on by high-volume surgeons (P<.001). Although total gynecologic surgical volume had no effect on cost, patients treated by high-volume vaginal surgeons had lower costs (P<.001). CONCLUSION: Perioperative morbidity and resource use are lower in women undergoing vaginal hysterectomy when the procedure is performed by high-volume vaginal surgeons.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".