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 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 | 0.000 | 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".