Impact of soft pancreas on pancreaticoduodenectomy outcomes and the development of the preoperative soft pancreas risk score
Bibliographic record
Abstract
Backgrounds/Aims: Pancreatic texture is difficult to predict without palpation. Soft pancreatic texture is associated with increased post-operative complications, including postoperative pancreatic fistula (POPF), cardiac, and respiratory complications. We aimed to develop a calculator predicting pancreatic texture using patient factors and to illustrate complications from soft pancreatic texture following pancreaticoduodenectomy. Methods: Data was collected from the 2016 to 2021 American College of Surgeons National Surgical Quality Improvement database including 17,706 pancreaticoduodenectomy cases. Patients were categorized into two cohorts based on pancreatic texture (9,686 hard, 8,020 soft). Multivariable modeling assessed the impact of patient factors on complications, mortality, and pancreatic texture. These preoperative factors were integrated into a risk calculator (preoperative soft pancreas risk score [PSPRS]) that predicts pancreatic texture. Results: < 0.001) were independently associated with a soft pancreas. PSPRS ≥6 correctly identified >40% of patients preoperatively as having a hard pancreas (68.9% specificity). Conclusions: A soft pancreas was independently associated with serious postoperative complications. Our results were integrated into a risk calculator predicting pancreatic texture from preoperative patient factors, potentially enhancing preoperative counseling and surgical decision-making.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".