Prognosis after surgery for multiple endocrine neoplasia type 1-related pancreatic neuroendocrine tumors: Functionality matters
Bibliographic record
Abstract
BACKGROUND: Metastasized pancreatic neuroendocrine tumors are the leading cause of death in patients with multiple endocrine neoplasia type 1. Aside from tumor size, prognostic factors of pancreatic neuroendocrine tumors are largely unknown. The present study aimed to assess whether the prognosis of patients with resected multiple endocrine neoplasia type 1-related nonfunctioning pancreatic neuroendocrine tumors differs from those with resected multiple endocrine neoplasia type 1-related insulinomas and assessed factors associated with prognosis. METHODS: Patients who underwent resection of a multiple endocrine neoplasia type 1-related pancreatic neuroendocrine tumors between 1990 and 2016 were identified in 2 databases: the DutchMEN Study Group and the International MEN1 Insulinoma Study Group databases. Cox regression was performed to compare liver metastases-free survival of patients with a nonfunctioning pancreatic neuroendocrine tumors versus those with an insulinoma and to identify factors associated with liver metastases-free survival. RESULTS: Out of 153 patients with multiple endocrine neoplasia type 1, 61 underwent resection for a nonfunctioning pancreatic neuroendocrine tumor and 92 for an insulinoma. Of the patients with resected lymph nodes, 56% (18/32) of nonfunctioning pancreatic neuroendocrine tumors had lymph node metastases compared to 10% (4/41) of insulinomas (P = .001). Estimated 10-year liver metastases-free survival was 63% (95% confidence interval 42%-76%) for nonfunctioning pancreatic neuroendocrine tumors and 87% (72%-91%) for insulinomas. After adjustment for size, World Health Organization tumor grade, and age, nonfunctioning pancreatic neuroendocrine tumors had an increased risk for liver metastases or death (hazard ratio 3.04 [1.47-6.30]). In pancreatic neuroendocrine tumors ≥2 cm, nonfunctioning pancreatic neuroendocrine tumors (2.99 [1.22-7.33]) and World Health Organization grade 2 (2.95 [1.02-8.50]) were associated with liver metastases-free survival. CONCLUSION: Patients with resected multiple endocrine neoplasia type 1-related nonfunctioning pancreatic neuroendocrine tumors had a significantly lower liver metastases-free survival than patients with insulinomas. Postoperative counseling and follow-up regimens should be tumor type specific and at least consider size and World Health Organization grade.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".