Association of body mass index with surgical complications after minimally invasive hysterectomy
Bibliographic record
Abstract
PURPOSE: To study the association between body mass index (BMI) and short-term postoperative complications of patients undergoing laparoscopic hysterectomy (LH). STUDY DESIGN: This is a cohort study of patients who underwent LH for benign conditions. We used prospectively collected data from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database from 2012 to 2020. We categorized patients into BMI subgroups and compared 30-day postoperative complication rates, defined by the Clavien-Dindo classification. RESULTS: , aOR 95% CI 1.13(1.07-1.19)] in the higher BMI group but no differences in major complications. When comparing obesity categories to the normal BMI group, class I, II, and III categories had a lower likelihood of major complications [aOR 95% CI 0.87(0.80-0.93), 0.84(0.77-0.91), 0.82(0.75-0.90), and 0.83(0.75-0.91), respectively] compared to normal weight individuals. Patients in class II and III categories had a higher likelihood of minor complications [aOR 95% CI 1.12(1.03-1.21), and 1.17(1.08-1.28), respectively] compared to normal weight individuals. The mean operative time was significantly longer for each BMI group compared to lower BMI groups (range 115.2-144.5 min, p < 0.05). CONCLUSIONS: Higher BMI was associated with a higher risk of any and minor complications than lower BMI in patients undergoing LH, as well as increased operative time. When comparing specific BMI categories, overweight and obesity categories were associated with lower risks of major complications compared to the normal BMI category. WHAT DOES THIS STUDY ADDS TO THE CLINICAL WORK?: Among women undergoing minimally invasive hysterectomy for benign indications, higher BMI classes were associated with lower risk of short-term postoperative complication compared to the normal BMI class. This information can be used in preoperative planning, counseling, and shared-decision making.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".