Functioning and nonfunctioning neuroendocrine tumors of the pancreas
Bibliographic record
Abstract
PURPOSE OF REVIEW: Neuroendocrine tumors of the pancreas are a small subgroup of tumors characterized by a variety of biological behaviors. Recent changes in their classification should help better define the prognosis of this diverse group of tumors. With recent advances in diagnosis and staging, the treatment options for all neuroendocrine tumors have evolved. Presented here is a review of the current-day knowledge for neuroendocrine tumors of the pancreas. RECENT FINDINGS: A consensus by leading experts in the neuroendocrine tumors field has proposed an algorithm for the diagnosis, treatment and follow-up of these rare tumors. Surgical resection remains the first-line therapy. Alternative forms of cytoreduction such as radiofrequency ablation and embolization, have increased the ability of the surgeon to debulk these tumors, resulting in improved survival and better palliation. Contrary to adenocarcinoma of the pancreas, hormonal and biotherapy offer unique treatment strategies for these rare tumors. Very recent developments utilizing radionuclide therapy hold promise for not only palliation, but may prove to be a beneficial form of adjuvant therapy. SUMMARY: Presented here is a summary of the recent literature on the diagnosis and treatment of neuroendocrine tumors of the pancreas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".