Immunoglobulin G4-Negative Inflammatory Pseudotumors of the Pancreas
Bibliographic record
Abstract
Inflammatory pseudotumor (IPT) can occur in any organ, but rarely shows pancreatic involvement. While surgical excision has been recommended as the primary treatment for IPT of the pancreas in the past, some authors suggest observation while medical management often results in regression. Corticosteroids, nonsteroidal anti-inflammatory drugs and immunosuppressive therapy have been used to treat IPTs. Spontaneous regression has also been reported in IPT managed without surgical intervention. A 62-year-old female was evaluated for worsening abdominal pain and a mass in the neck of the pancreas that was identified on ultrasound. Further imaging with magnetic resonance imaging revealed a pancreatic mass with dilated pancreatic duct and an atrophic parenchyma of the pancreatic neck. Her serum tumor markers were not elevated. As this lesion appeared to be resectable pancreatic cancer based on cross-sectional imaging, no biopsy was performed prior to surgical resection. Distal pancreatectomy and splenectomy was recommended and the patient desired to proceed. Her recovery was uneventful with no postoperative complications, including pancreatic fistula. Final pathology revealed a lesion consistent with the diagnosis of immunoglobulin G4 (IgG4)-negative IPT without neoplasm. IPT of the pancreas is a difficult entity to diagnose and treat due to clinical and imaging characteristics closely resembling pancreatic adenocarcinoma. Biopsy with immunohistochemical analysis can be useful in diagnosing IPT; however, symptomatic lesions and concerning findings on cross-sectional imaging may warrant more definitive surgical intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".