Vascular Resection for Pancreas Cancer
Bibliographic record
Abstract
OBJECTIVE BACKGROUND: Combined pancreatic and vascular resections are increasingly performed for pancreatic ductal adenocarcinoma (PDAC). We evaluated the outcomes after pancreatectomy with nonvascular resection (NVR), venous resection (VR), and arterial resection (AR). METHODS: Retrospective review (2011-2023) of 715 patients with PDAC treated with curative-intent surgery. Associations among clinicopathological data, perioperative therapy, time to recurrence (TTR), and overall survival (OS) were evaluated. RESULTS: Initial staging revealed 533 resectable, 98 borderline, and 84 locally advanced PDAC cases. Pancreaticoduodenectomy was the most common procedure (n = 467). NVR was performed in 351 (58.2%) patients, VR in 181 (30.0%), and AR in 70 (11.8%). The median TTR and OS did not significantly differ according to the initial staging or type of pancreas resection. Median TTR and OS were significantly shorter for VR (14.5 and 22.7 months) compared with NVR (18.6 and 30.5 months, P < 0.001) and AR (20.6 and 30.9 months, P = 0.004 and P = 0.017). Chemotherapy or chemoradiation significantly prolonged TTR (20.1 vs 10.2 months, P < 0.001 and 25.3 vs 16.4 months, P < 0.001) and OS (31.5 vs 17.2 months, P < 0.001 and 35.5 vs 27.5 months, P = 0.030). AR was associated with higher 90-day mortality rates. In the multivariable analysis, vascular resection was not associated with OS. Perioperative therapy, pathologic N0 status, and absence of perineural invasion were the key predictors of longer TTR and OS. CONCLUSIONS: Pancreatectomy with AR was not associated with worse oncological outcomes when controlling for perioperative therapy. However, AR was associated with higher 90-day mortality rates. Patient selection is crucial when performing AR in patients with PDAC.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".