Pediatric MR Cholangiopancreatography: Principles, Technique, and Clinical Applications
Bibliographic record
Abstract
High-quality magnetic resonance (MR) cholangiopancreatographic images are difficult to obtain in children due to the small caliber of the pediatric bile ducts and to motion artifacts. However, there has been ongoing improvement in image quality, thanks to better coil technology, increased speed of acquisition, refinement in respiratory compensation techniques, and newer sequences. Heavily T2-weighted fast spin-echo (FSE) and single-shot FSE MR imaging sequences with long echo times are used to image the biliary and pancreatic ducts. Secretin has been shown to improve the visualization of the pancreatic duct and pancreaticobiliary junction. Factors that affect image quality in pediatric MR cholangiopancreatography include sedation, negative oral contrast material, radiofrequency coil selection, respiratory compensation techniques, echo time, echo train length, section-slab thickness, planes of scanning, field of view, and number of signals acquired. However, giving proper attention to these factors and tailoring the study to the body size of the patient (which varies considerably) can lead to high-quality diagnostic MR cholangiopancreatographic images. Use of MR cholangiopancreatography in children is limited by the need for sedation or anesthesia, high cost, limited availability, and long scanning times. Nonetheless, this modality can be a viable alternative to endoscopic retrograde cholangiopancreatography (ERCP) in the evaluation of various entities such as choledochal cyst, recurrent pancreatitis, primary sclerosing cholangitis, and a transplanted liver, and may obviate ERCP.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 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".