Wound dehiscence in a sample of 1 776 cystectomies: identification of predictors and implications for outcomes
Bibliographic record
Abstract
OBJECTIVE: To investigate the incidence and predictors of wound dehiscence in patients undergoing radical cystectomy (RC). PATIENTS AND METHODS: In all, 1 776 patient records with Current Procedural Terminology (CPT) codes for radical cystectomy (RC) were extracted from the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) between 2005 and 2012. Stratification was made based on the occurrence of postoperative wound dehiscence, defined as loss of integrity of fascial closure. Descriptive and logistic regression models were used to identify predictors of postoperative wound dehiscence. The implications of wound dehiscence on peri- and postoperative outcomes such as complications, mortality, prolonged length of stay (>11 days), and prolonged operative time (>411 min), were assessed. RESULTS: Of 1 776 patients analysed, 57 (3.2%) had a documented wound dehiscence. In multivariable analyses, chronic obstructive pulmonary disease (odds ratio [OR] 2.0, 95% confidence interval [CI] 1.0-4.0; P = 0.03) and high body mass index (OR 2.3, 95% CI 1.3-4.4; P = 0.008) were significant predictors of wound dehiscence. While female gender had significantly lower proportions of wound dehiscence, multivariable analyses did not confirm this (OR 0.4, 95% CI 0.4-1.4; P = 0.75). CONCLUSIONS: Our study is the first to identify predictors of wound dehiscence after RC in a large, contemporary multi-institutional cohort. Identifying patients at risk of postoperative wound complications may guide the use of preventative measures at the time of surgery.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".