A Retrospective Study on Postoperative Complications in Gynecological Surgeries: Identification of High-Risk Factors and Best Practices
Bibliographic record
Abstract
Background: Laparoscopic gynecologic surgery is widely favored for its minimally invasive nature, offering reduced postoperative pain, shorter hospital stays, and faster recovery. However, despite its advantages, postoperative complications—ranging from minor infections to major injuries—remain a concern. Identifying patient- and procedure-specific risk factors is critical to enhancing surgical safety and outcomes. Objective: To evaluate the incidence and predictors of postoperative complications in gynecologic laparoscopic surgeries and identify high-risk patient and procedural factors using a large, retrospective dataset. Methods: This retrospective cohort study included 15,308 patients who underwent laparoscopic gynecologic procedures at tertiary care hospitals in Pune, India, between January 2023 and October 2024. Patients were categorized by procedure type: adnexal surgery, myomectomy/uterine lesion surgery, LAVH/TLH, and malignancy surgery. Data on demographics, prior surgical history, comorbidities, and surgical details were collected. Complications were classified as major (e.g., bowel or ureteral injury, hemorrhage requiring reoperation) or minor (e.g., infection, transient fever). Multivariate logistic regression identified independent risk factors for major complications. Results: The overall major complication rate was 0.51%, and the minor complication rate was 4.64%. Surgeries for malignancy had the highest major complication rate. Independent risk factors for major complications included age 31–60 years (aOR: 2.88; 95% CI: 1.89–7.88), age >60 years (aOR: 2.92; 95% CI: 1.67–5.65), prior abdominal surgery (aOR: 3.58; 95% CI: 1.38–6.54), obesity (aOR: 2.52; 95% CI: 1.39–7.28), and higher surgical complexity (e.g., malignancy surgery vs. adnexal: aOR: 7.62; 95% CI: 3.61–13.63). Conclusion: Although complication rates in laparoscopic gynecologic surgery remain low, advanced age, obesity, previous abdominal surgery, and complex procedures significantly increase the risk of major complications. These findings underscore the need for thorough preoperative assessment, individualized surgical planning, and targeted risk mitigation strategies to optimize patient outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".