Incisional Hernia Following Open Pancreaticoduodenectomy: Incidence and Risk Factors at a Tertiary Care Centre
Bibliographic record
Abstract
(1) Background: Incisional hernia (IH) is one of the most common complications following open abdominal surgery. There is scarce evidence on its real incidence following pancreatic surgery. The purpose of this study is to evaluate the incidence and the risk factors associated with IH development in patients undergoing pancreaticoduodenectomy (PD). (2) Methods: We retrospectively reviewed all patients undergoing PD between 2014 and 2020 at our centre. Data were extracted from a prospectively held database, including perioperative and long-term factors. We performed univariate and multivariate analysis to detect those factors potentially associated with IH development. (3) Results: The incidence of IH was 8.8% (19/213 patients). Median age was 67 (33–85) years. BMI was 24.9 (14–41) and 184 patients (86.4%) underwent PD for malignant disease. Median follow-up was 23 (6–111) months. Median time to IH development was 31 (13–89) months. Six (31.5%) patients required surgical repair. Following univariate and multivariate analysis, preoperative hypoalbuminemia (OR 3.4, 95% CI 1.24–9.16, p = 0.01) and BMI ≥ 30 kg/m2 (OR 2.6, 95% CI 1.06–8.14, p = 0.049) were the only factors independently associated with the development of IH. (4) Conclusions: The incidence of IH following PD was 8.8% in a tertiary care center. Preoperative hypoalbuminemia and obesity are independently associated with IH occurrence following PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".