Review of Current Applications of Immunohistochemistry in Pediatric Nonneoplastic Gastrointestinal, Hepatobiliary, and Pancreatic Lesions
Bibliographic record
Abstract
Immunohistochemical (IHC) stains are widely used by pathologists for a variety of considerations in the diagnostic workup of pediatric nonneoplastic lesions in gastrointestinal (GI), hepatic, biliary, and pancreatic lesions. The pathologic changes cover a wide range and types of presentations, including inflammatory (bacterial and viral), metaplastic, posttransplant lymphoproliferative, autoimmune, metabolic, degenerative, developmental, and genetic conditions, among others. The everyday practical value of IHC stains covers primary identification, confirmation, differential, and/or exclusionary roles in the hands and eyes and minds of the practitioners. This article is intended to review and discuss the currently available IHC stains for a variety of pediatric GI, hepatobiliary, and pancreatic lesions as encountered in the day-to-day practice of pathologists and clinicians. It reflects the most recent methods and types of IHC stains with the stated aim of helping to provide a quick reference for diagnostic considerations and thereby facilitate the workup of a broad range of GI and related conditions in a pediatric population. The tables provide a handy reference on a wide range of IHC stains for commonly encountered lesions covering a variety of pediatric GI, hepatobiliary, and pancreatic conditions that are amenable to light microscopic diagnostic interpretation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".