Risk factors for major complications following pelvic exenteration: A NSQIP study
Bibliographic record
Abstract
OBJECTIVES: Due to the rarity of pelvic exenteration surgery, it is challenging to predict which patients are at an increased risk for postoperative complications. We aimed to study the predictors for postoperative complications among women undergoing pelvic exenteration for gynecologic malignancy. METHOD: We used the National Surgical Quality Improvement Program registry to evaluate postoperative course and complications of those patients undergoing pelvic exenteration in the period 2012-2022. The primary objective of the analysis was to estimate the major postoperative complications following pelvic exenteration. RESULTS: Overall, 794 pelvic exenterations were included. Of those, 56.5 % were anterior exenteration, 43.5 % were posterior exenteration, and 13.9 % were a combined exenteration. The rate of minor complications was 72.5 % (n = 576), and the rate of major complications was 31.5 % (n = 250). The most common minor complications were blood transfusion (n = 538, 67.8 %), followed by superficial surgical site infections (SSI) and urinary tract infections (9.8 % and 9.4 %, respectively). Among the major complications, the most common was organ/space SSI (11.2 %), followed by sepsis (9.2 %), reoperation (8.6 %), and wound dehiscence (5.2 %). Death within 30 days occurred in 1.5 % of patients. In multivariable regression analysis, the following factors were independently associated with major complications: higher BMI [adjusted odds ratio (aOR) 1.03 95 % confidence interval (CI) (1.01-1.05)], diabetes [aOR 1.82 95 % CI (1.13-3.22)], low serum albumin [aOR 0.73 95 % CI (0.54-0.98)], and high serum creatinine [aOR 1.70 95 % CI (1.05-2.77)]. CONCLUSIONS: Major postoperative complications occur in approximately one third of pelvic exenterations for gynecologic malignancies. Our study highlights independent factors associated with major postoperative complications, of which some are potentially modifiable.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".