Surgical outcomes of patients with duodenal vs pancreatic neuroendocrine tumors following pancreatoduodenectomy
Bibliographic record
Abstract
BACKGROUND: To investigate the short- and long-term outcomes of patients undergoing pancreaticoduodenectomy (PD) for duodenal neuroendocrine tumors (dNETs) vs pancreatic neuroendocrine tumors (pNETs). METHOD: Patients undergoing PD for dNETs or pNETs between 1997 and 2016 were identified from a multi-institutional database. Overall survival (OS) and recurrence-free survival (RFS) were evaluated. RESULTS: Among 276 patients who underwent PD, 244 (88.4%) patients had a primary pNET, whereas 32 (11.6%) patients had a dNET. Following PD, postoperative morbidity and mortality were comparable. While the total number of lymph nodes examined was similar between the two groups (median, dNETs 15.0 vs pNETs 13.0; P= .648), patients with dNETs had a higher incidence of lymph node metastasis (LNM) (60.0% vs 38.2%; P = .022) and a larger number of metastatic nodes (median, 3.5 vs 2.0; P = .039). No differences in OS or RFS were noted among patients with dNETs vs pNETs in both unadjusted and adjusted analyses. Among patients who recurred after PD, patients with dNETs were more likely to recur early (within 2 years, 100% vs 49.2%; P = .029) and at an extrahepatic site (intrahepatic-only recurrence, 20.0% vs 54.1%; P = 0.142) vs patients with pNETs. CONCLUSIONS: Patients with dNETs and pNETs had a similar prognosis following PD. Data on differences in the incidence of LNM, as well as in recurrence time and patterns may help to inform the treatment of these patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".