Pancreatic Neuroendocrine Tumors in the 21<sup>st</sup> Century—An Update
Bibliographic record
Abstract
Pancreatic neuroendocrine tumors (PNETs) are rare, reported to account for less than 1% - 2% of all pancreatic tumors. This, however, is likely an underestimation, as improved radiologic techniques and heightened awareness have resulted in an increase in the detection of incidentalomas, with estimations of true prevalence as high as 10%. The term “PNET” is an umbrella name that encompasses a heterogeneous group of neoplasms each with distinct clinical presentations, diagnostic radiographic features, management principles, and tumor/patient outcomes. In this context, accurate diagnosis is challenging, and management guidelines remain unclear. A high degree of clinical suspicion is required for best patient management. This manuscript provides an update on PNETs in the 21st century, in which we re-examine the terminology, epidemiology, classification, etiopathogenesis, radiographic and histopathologic diagnostic features, management for localized and metastatic disease, as well as a review of features defining functional and non-functional PNETS, and finally deliberates on the prognosis and predictive features of this unpredictable and largely unfathomable neoplasm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".