Rol del diagnóstico por imágenes en la pancreatitis autoinmune
Bibliographic record
Abstract
Autoimmune pancreatitis is a specific form of chronic recurrent pancreatitis whose etiopathogenesis involves immunological mechanisms. Historically, two varieties of the disease have been described, each with clinical, biochemical and histological particularities. Recently, a third subtype has been described as a consequence of the adverse effects associated with immunotherapy, which is increasingly indicated for several tumors. The diagnosis of autoimmune pancreatitis is a challenge for professionals, in part because of its heterogeneous clinical presentation, which includes obstructive jaundice in type 1 autoimmune pancreatitis and abdominal pain in type 2, and can mimic more serious pathologies such as pancreatic ductal adenocarcinoma. Serum biomarkers (IgG4) play an important role in the diagnosis, but they can sometimes be normal, especially in type 2 autoimmune pancreatitis, or be elevated in other pathologies. Non-invasive imaging techniques, in particular magnetic resonance cholangiopancreatography with intravenous contrast, play a central role in the diagnostic process of the disease. In this article, we review the imaging aspects of autoimmune pancreatitis and the extrapancreatic manifestations of systemic IgG4 disease, the features that differentiate it from pancreatic cancer and we evaluate the radiological criteria for response to corticosteroid treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".