Total Compared with Partial Pancreatectomy for Pancreatic Adenocarcinoma: Assessment of Resection Margin, Readmission Rate, and Survival from the U.S. National Cancer Database
Bibliographic record
Abstract
Introduction: Total pancreatectomy for pancreatic ductal adenocarcinoma has historically been associated with substantial patient morbidity and mortality. Given advancements in perioperative and postoperative care, evaluation of the surgical treatment options for pancreatic adenocarcinoma should consider patient outcomes and long-term survival for total pancreatectomy compared with partial pancreatectomy. Methods: The U.S. National Cancer Database was queried for patients undergoing total pancreatectomy or partial pancreatectomy for pancreatic adenocarcinoma during 1998–2006. Demographics, tumour characteristics, operative outcomes, 30-day mortality, 30-day readmission, additional treatment, and Kaplan–Meier survival curves were compared. Results: The database query returned 807 patients who underwent total pancreatectomy and 5840 who underwent partial pancreatectomy. More patients who underwent total pancreatectomy than a partial pancreatectomy had a margin-negative resection (p < 0.0001). Mortality and readmission rates were similar in the two groups, as was long-term survival on Kaplan–Meier curves (p = 0.377). A statistically significant difference in the rate of surgery only (without additional treatment) was observed for patients in the total pancreatectomy group (p = 0.0003). Conclusions: Although total compared with partial pancreatectomy was associated with a higher rate of margin-negative resection, median survival was not significantly different for patients undergoing either procedure. Patients who underwent total pancreatectomy were significantly less likely to receive adjuvant therapy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".