Bibliographic record
Abstract
AIMS/HYPOTHESIS: Protein gene product 9.5 (PGP 9.5) is a marker for neuroendocrine cells but has not been used for pancreatic islet cells and pancreatic endocrine tumors (PETs). Antibodies for PGP 9.5 are now commercially available for immunocytochemical study, with which immunostaining may be able to differentiate between benign and malignant PETs. RESULTS: All 4 kinds of normal islet cells were positively immunostained for PGP 9.5-moderately positive for β-cells and strongly positive for δ-cells, whereas ganglion cells were immunostained more strongly than islet cells. Nine of 12 insulinomas were moderately to strongly positive for PGP 9.5. Two glucagonomas, 3 of 6 pancreatic polypeptidomas (PPomas), 3 of 9 gastrinomas, and 2 of 4 non-functioning PETs were negative for PGP 9.5. MATERIALS AND METHODS: Thirty-four PETs were immunocytochemically stained for PGP 9.5 using a rabbit polyclonal antibody together with immunostaining for 4 pancreatic hormones, chromogranin A (CgA), and gastrin. PETs consisted of 12 insulinomas, 2 glucagonomas, 1 somatostatinoma (SRIFoma), 6 PPomas, 9 gastrinomas, and 4 non-functioning PETs. CONCLUSION/INTERPRETATION: PGP 9.5 immunostaining was universally positive for 4 kinds of islet cells and was moderately to strongly positive for 9 of 12 (75%) insulinomas. All 22 non-β-cell PETs were negative or weakly positive for PGP 9.5, and thus negative or weakly positive PGP 9.5 immunostaining may be used as a marker for potential malignancy and poor prognosis for non-β-cell PETs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".