Risk factors for major complications following colorectal resections for endometriosis in the USA
Bibliographic record
Abstract
PURPOSE: We aimed to describe the incidence and identify risk factors for the occurrence of short-term major posto-perative complications following colorectal resection for endometriosis. METHODS: A cohort study using data from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database from 2012-2020. We included patients with a primary diagnosis of endometriosis who underwent colon or rectal resections for endometriosis. RESULTS: Of 755 women who underwent colorectal resection, 495 (65.6%) had laparoscopic surgery and 260 (34.4%) had open surgery. The major complication rate was 13.5% (n = 102). Women who underwent open surgery had a higher proportion of major complications (n = 53, 20.4% vs. n = 49, 9.9%, p < 0.001). In a multivariable regression analysis, Black race (aOR 95%CI 2.81 (1.60-4.92), p < 0.001), Hispanic ethnicity (aOR 95%CI 3.02 (1.42-6.43), p = 0.004), hypertension (aOR 95%CI 1.89 (1.08-3.30), p = 0.025), laparotomy (aOR 95%CI 1.64 (1.03-3.30), p = 0.025), concomitant enterotomy (aOR 95%CI 3.02 (1.26-7.21), p = 0.013), and hysterectomy (aOR 95%CI 2.59 (1.62-4.15), p < 0.001) were independently associated with major post-operative complications. In a subanalysis of laparoscopies only, Hispanic ethnicity, chronic hypertension, lysis of bowel adhesions, and hysterectomy were independently associated with major complications. In a subanalysis of laparotomies only, Black race and hysterectomy were independently positively associated with the occurrence of major complications. CONCLUSION: This study provides a current population-based estimate of short-term complications after surgery for colorectal endometriosis in the USA. The identified risk factors for complications can assist during preoperative shared decision-making and informed consent process.
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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 | 0.001 | 0.024 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".