Cystic lesions of the pancreatico-biliary tree: A schematic MRI approach
Bibliographic record
Abstract
Although a common occurrence, cystic lesions of the pancreatico-biliary tree (PBT) may pose a diagnostic dilemma because they encompass a large number of neoplastic and benign processes with varied clinical symptoms. Knowledge of lesion classification and characterization are essential in making an accurate prospective diagnosis. This is necessary for identifying clinically significant cystic masses, which at times may require invasive intervention from indolent, nonneoplastic lesions, for which surveillance may suffice. Today, there is an arsenal of modalities for assessing the PBT, however, magnetic resonance imaging (MRI) remains at the forefront for characterizing cystic morphology and fluid content, internal septations, solid component, enhancement patterns, as well as assessing the surrounding normal structures. This pictorial review aims to review the spectrum of MRI features, which will aid in the differential diagnoses of cystic lesions of the PBT and mimickers, enabling the radiologist to reach a more confident diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".