Major Perioperative Complications of Benign Gynecologic Procedures at a University-Affiliated Hospital
Bibliographic record
Abstract
Background: With the increasing use of minimally invasive techniques for gynecologic procedures, women are at a low risk for peri-operative complications. The purpose of this study was to determine the incidence of and risk factors for major intra or postoperative complications among women undergoing benign gynecologic surgeries. Methods: We conducted a retrospective observational study of all women who underwent benign gynecologic surgery in 2016-2017 at a University-Affiliated community hospital. Pregnant women, malignancy cases, and hysteroscopic or minor vulvar procedures were excluded. Primary outcome was composite intraoperative and/or 30-day postoperative complications requiring medical or surgical management. Logistic regression identified significant patient, peri-operative and surgeon risk factors associated with complications. Results: Of 975 patients included, 53 patients experienced major intra or postoperative complications (5.4%). Mean age was 47.7 ± 13.8 years. Mean BMI was 27.1 ± 5.8 kg/m2. Prior abdominal surgery (laparotomy or laparoscopy) (adjusted odds ratio [OR]= 2.01, 95%CI 1.05-3.83) and emergency surgery (adjusted OR= 19.54, 95%CI 2.99-127.54) were significantly associated with major complications. Surgeon volume of 1-2 operative days per month (adjusted OR=0.30, 95%CI 0.10 - 0.87) and age 40-64 years (adjusted OR=0.24, 95%CI 0.11- 0.56) had a protective effect on the risk of major complications. Conclusions: Among patients in our sample, 5.4% experienced major complications from a benign gynecologic surgery. Complications from benign gynecologic surgery are rare, even in the absence of robotic equipment. Center-specific data and a discussion of the increased morbidity associated with with prior abdominal surgery and emergency surgery should be considered for pre-operative patient counselling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".