Gastrinoma of Cystic Duct: A Rare Association With Multiple Endocrine Neoplasia Type 1
Bibliographic record
Abstract
Neuroendocrine tumors (NETs) of cystic duct are extremely rare, accounting for less than 2% of NET cases. The association of biliary tree NET and multiple endocrine neoplasm type 1 (MEN1) are even more rare. In this report, we described a case of a 65-year-old woman who was referred to our neuroendocrine outpatient clinic to investigate MEN1 after an incidental diagnosis of gastrinoma. Her medical history initiated 7 years earlier with severe peptic disease not responsive to proton pump inhibitor therapy. Endoscopic study revealed erosive antral gastritis, erosive duodenitis, bulbar ulcer and pyloric deformity. During follow-up she presented with abdominal pain, chronic diarrhea and weight loss; an ultrasonography was performed and showed only a cholelithiasis. She underwent a video laparoscopic cholecystectomy and all her symptoms were solved. Histopathological study found a 1.0 cm well differentiated NET (Ki-67 labeling index < 2%) located in cystic duct infiltrating the entire wall and subserosa. The MEN1 investigation revealed a primary hyperparathyroidism with a brown tumor in right iliac bone; the patient was referred to a total parathyroidectomy with autotransplantation. No evidence of pituitary tumor was found. The patient remains asymptomatic 24 months after surgery. To conclude, this case highlights an unusual presentation of a cystic duct primary NET gastrinoma in a MEN1 context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".