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Record W3112121624 · doi:10.1136/jclinpath-2020-207029

Pathology of IgG4-related sclerosing mastitis

2020· review· en· W3112121624 on OpenAlexaff
Polycarp Erivwo, Gulisa Turashvili

Bibliographic record

VenueJournal of Clinical Pathology · 2020
Typereview
Languageen
FieldMedicine
TopicIgG4-Related and Inflammatory Diseases
Canadian institutionsUniversity of TorontoMount Sinai HospitalMemorial University of Newfoundland
Fundersnot available
KeywordsIgG4-related diseaseMedicineMastitisDifferential diagnosisPathologyDiseaseBreast cancerCancerInternal medicine

Abstract

fetched live from OpenAlex

Immunoglobulin G4-related sclerosing mastitis (IgG4-RM) is a recently recognised member of the IgG4-related disease (IgG4-RD) family, a multisystem fibroinflammatory condition that can affect any organ system. IgG4-RM is rare and predominantly occurs in middle-aged women. It may present with painless palpable mass and/or lymphadenopathy thereby mimicking breast cancer. Although there is an abundance of literature describing the clinicopathological characteristics of IgG4-RD in a variety of organs, data on IgG4-RM are limited due to its rarity. This review describes the manifestation of the disease in the breast based on reported cases, emphasising the clinicopathological features, pathophysiology, differential diagnosis, treatment and prognosis.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.151
GPT teacher head0.459
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations15
Published2020
Admission routes1
Has abstractyes

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