Impact of Wound Protectors on Complications Following Pancreaticoduodenectomy: A National Surgical Quality Improvement Program Analysis of 20 960 Patients
Bibliographic record
Abstract
BACKGROUND: Wound protectors (WPs) have been shown to decrease postoperative wound complications, yet limited data exist supporting WP for pancreaticoduodenectomies, with limited uptake in practice. We evaluated the effect of WP in pancreaticoduodenectomies on surgical site infections (SSIs) and serious complications. METHODS: Utilizing the National Surgical Quality Improvement Program database, we included patients undergoing pancreaticoduodenectomy between 2017 and 2021. Baseline demographics and complications were compared between WP and no WP cohorts. Multivariate logistic regression was performed to identify the effect of WP use on 30-day complications and factors associated with WP use. RESULTS: Of 20 960 patients, 6167 (29.4%) used a WP. WPs were more commonly used in lower ASA classes, more comorbid patients, and preoperative weight loss. WP use was associated with increased operative time but decreased length of stay, SSIs, organ space infection, pancreatic fistula, reoperation, and serious complication. WP was independently associated with decreased serious complication (aOR 0.80, p < 0.001) and SSI (aOR 0.57, p < 0.001). Factors associated with increased likelihood of WP use include preoperative weight loss, broad-spectrum antibiotic use, absence of bleeding disorder and firmer pancreatic texture. CONCLUSION: WP use during pancreaticoduodenectomy is associated with decreased number of postoperative complications and SSI. Future prospective randomized studies should assess cost-benefit and barriers to use to increase uptake of WP use.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".