Liver transplant for primary biliary tract neuroendocrine tumor in a nine‐year‐old girl
Bibliographic record
Abstract
BACKGROUND: Neuroendocrine tumors (NETs) are rare epithelial neoplasms that arise most commonly from the gastrointestinal tract. In pediatrics, the most common site of origin is in the appendix, with the liver being the most common site of metastasis. Neuroendocrine tumors arising from the biliary tract are extremely rare. METHODS: We describe a case of a nine-year-old girl who presented with obstructive cholestasis and was found to have multiple liver masses identified on biopsy as well-differentiated neuroendocrine tumor with an unknown primary tumor site. RESULT: The patient underwent extensive investigation to identify a primary tumor site, including endoscopy, endoscopic ultrasound, and capsule endoscopy. The patient ultimately underwent definitive management with liver transplant, and on explant was discovered to have multiple well-differentiated neuroendocrine tumors, WHO Grade 1, with extensive infiltration into the submucosa of bile duct, consistent with primary biliary tract neuroendocrine tumor. CONCLUSION: Identifying the site of the primary tumor in NETs found within the liver can be challenging. To determine if an extrahepatic primary tumor exists, workup should include endoscopy, EUS, and capsule endoscopy. Children with well-differentiated hepatic NETs, with no identifiable primary tumor, and an unresectable tumor, are considered favorable candidates for liver transplantation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".