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Record W2606158190 · doi:10.1177/1177390117690140

Review of Current Applications of Immunohistochemistry in Pediatric Nonneoplastic Gastrointestinal, Hepatobiliary, and Pancreatic Lesions

2017· review· en· W2606158190 on OpenAlexaff
Joseph de Nanassy, Dina El Demellawy

Bibliographic record

VenueAnalytical Chemistry Insights · 2017
Typereview
Languageen
FieldMedicine
TopicGastrointestinal disorders and treatments
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicinePathologyDifferential diagnosisImmunohistochemistryGastrointestinal pathologyPediatric gastroenterologyDisease

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.381
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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