Multimodality Imaging of Neoplastic and Nonneoplastic Solid Lesions of the Pancreas
Bibliographic record
Abstract
Solid lesions of the pancreas represent a heterogeneous group of entities that can be broadly classified as either neoplastic or nonneoplastic. Neoplastic lesions include pancreatic adenocarcinoma, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, pancreatoblastoma, pancreatic lymphoma, metastases to the pancreas, and rare miscellaneous neoplasms. Nonneoplastic lesions include focal pancreatitis, fatty infiltration-replacement, intrapancreatic accessory spleen, congenital anomalies such as prominent pancreatic lobulation and bifid pancreatic tail (pancreatic bifidum), and rare miscellaneous lesions (eg, pancreatic sarcoidosis, Castleman disease of the pancreas). A variety of imaging modalities are available for assessing these solid lesions, including ultrasonography (US), computed tomography (CT), magnetic resonance imaging, endoscopic US, and hybrid nuclear imaging techniques such as single photon emission computed tomography-CT and positron emission tomography-CT, each of which has its own strengths and limitations. Accurate diagnosis can be challenging, and use of a multimodality imaging approach is often helpful in equivocal or complex cases. Knowledge of relevant clinical information and key radiologic features is essential for confident lesion characterization and differentiation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".