Cytology‐Radiology Correlation Series: Pancreatic cytopathology
Bibliographic record
Abstract
The prevalence of pancreatic lesions has increased over the years because of an increase in accessibility to and the quality of cross-sectional imaging. This commentary describes the common non-neoplastic and neoplastic pancreatic lesions. The images in this commentary depict classic cross-sectional images, sonographic findings, and the cytopathologic diagnosis of each lesion. Most common non-neoplastic lesions include pseudocysts, autoimmune pancreatitis, and chronic pancreatitis. Most common neoplastic lesions include serous cystadenomas, intraductal papillary mucinous neoplasms, mucinous cystic neoplasms, solid pseudopapillary neoplasms, neuroendocrine tumors, pancreatic ductal adenocarcinoma, acinar cell carcinoma, and metastases to the pancreas. The aim of this Cytoimaging Correlation Series is to demonstrate the multidisciplinary involvement in the diagnosis of pancreatic pathology and to highlight main findings in the most common entities found in everyday practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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 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".