Treatment Options for Pancreatic Neuroendocrine Tumors
Bibliographic record
Abstract
The management of pancreatic neuroendocrine tumors (PanNETs) involves classification into non-functional or functional PanNET, and as localized or metastatic PanNET. In addition, while most PanNETs are sporadic, these endocrine neoplasms can also be manifestations of genetic syndromes. All these factors may assist in forming a risk stratification system permitting a tailored management approach. Most PanNETs are classified as non-functional because they are not associated with clinical sequelae of hormone excess. They are characterized by non-specific symptoms, such as abdominal pain or weight loss, resulting from mass effect related to the pancreatic tumor or secondary to distant metastases. Accurate staging of the disease is essential for determining the appropriate approach to therapy. As cure is only potentially possible with surgical resection of the tumor, it is recommended to remove all localized and limited metastatic disease. However, many patients present with metastatic and/or advanced local disease. In such instances, the goal of therapy is to control tumor growth and/or decrease tumor burden, lengthen survival, and palliate local symptoms and those of hormone excess. This typically requires a multimodal approach, including surgery, liver-directed treatment, and systemic medical 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".