Multimodality Imaging of Neoplastic and Nonneoplastic Solid Lesions of the Pancreas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it