Pancreatic polypeptide secreting tumors – an ins- titutional experience and review of the literature
Bibliographic record
Abstract
Objectives: We present a retrospective analysis of patients with pancreatic neuroendocrine tumors (PNETs) who have had Pancreatic Polypeptide testing in an attempt to better define Pancreatic Polypeptide producing tumors as an entity and the role of Pancreatic Polypeptide (PP) as a biomarker. To our knowledge, this is the first single center comprehensive review of Pancreatic Polypeptide producing tumors. Methods: A retrospective study of patients with pancreatic neuroendocrine tumors seen at our institution from 1980 to 2011. All patients that have had PP concentrations measured at least once were evaluated. Data relating to diagnosis, pathology, surgery, liver directed therapies, chemotherapy and survival outcome were noted. Results: 71 patients with PNETs fulfilled the inclusion criteria (8 PPomas, 22 PP producing tumors and 41 non -PP producing tumors). We identified a trend towards better survival for patients with PP producing tumors vs. non- PP producing tumors ( p =0.19). There was no correlation between survival and a diagnosis of PPoma in relation to other PP producing tumors or non-PP producing tumors. There was a borderline significant positive correlation of PP in association with Chromogranin A in a postoperative setting ( p =0.061). Conclusions: Pancreatic Polypeptide is a biomarker that is worth prospective investigation and a standardized assay. Our analysis investigating Pancreatic Polypeptide as a prognostic and or predictive biomarker reveals a trend towards showing these characteristics. Using a standardized test and investigating this biomarker prospectively could lead to the validation of Pancreatic Polypeptide as a biomarker.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".