Revision 2: an immunohistochemical approach and evaluation of solid pseudopapillary tumour of the pancreas
Bibliographic record
Abstract
Solid pseudopapillary tumours (SPT) of the pancreas are uncommon, but with widespread and increased imaging, several of these lesions are coming to light incidentally and are subject to needle biopsies. On limited material and especially the solid or clear cell, variants of SPT can morphologically mimic most notably pancreatic neuroendocrine tumours and even metastatic renal cell carcinoma or melanoma. In this context, immunohistochemistry is important and useful in helping to reach the correct diagnosis. Several antibodies have been used in the immunohistochemical evaluation of SPT. As with most tumours, no one marker is specific, but rather a core panel is advocated. Recently, both beta-catenin and E-cadherin have been shown to be of value in SPT. Nuclear and cytoplasmic decoration of tumour cells by beta-catenin is seen in almost 100% of cases. This protein relocalisation away from the cell membrane is underscored by mutations of the beta-catenin gene. Mutations of the CDH1 gene are very uncommon in SPT, but the immunohistochemically detected changes to the protein are consistent and present in 100% of cases. Using an E-cadherin antibody to the extracellular domain of the molecule results in complete membrane loss, while the antibody directed to the cytoplasmic fragment produces distinct nuclear staining of the tumour cells. In addition, there is concordance of staining abnormalities between the two antibodies. When combined with CD10 and progesterone receptor positivity, a diagnosis of SPT can be rendered with confidence even in small biopsy samples.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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".