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Record W2751536174 · doi:10.1097/icu.0000000000000427

IgG4-related disease in the eye and ocular adnexa

2017· review· en· W2751536174 on OpenAlexaff
Larissa Derzko-Dzulynsky

Bibliographic record

VenueCurrent Opinion in Ophthalmology · 2017
Typereview
Languageen
FieldMedicine
TopicIgG4-Related and Inflammatory Diseases
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineIgG4-related diseaseScleritisRituximabUveitisPrednisoneLacrimal glandDiseasePathologyDermatologyOphthalmologySurgeryLymphoma

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: IgG4-related disease is a multi-organ fibro-inflammatory disease with characteristic histopathology showing lymphoplasmacytic infiltration, increased IgG4+ plasma cells and elevated IgG4/IgG ratios (>40%). The lacrimal gland is the most common ocular site of involvement. Scleritis and intraocular involvement in IgG4-related ophthalmic disease (IgG4-ROD) have recently been reported. The purpose of this review is to describe orbital and intraocular IgG4-ROD with a focus on publications since 2016. RECENT FINDINGS: Case reports of scleritis and uveitis in IgG4-ROD have been described since 2012. Systemic prednisone is recommended as the first-line treatment, but immunosuppressive therapy may be required for steroid-sparing or in steroid-resistant cases. High rates of systemic IgG4-RD involvement exist in patients with bilateral IgG4-ROD or if the lacrimal gland is involved. Rituximab is the most specific immune targeted therapy available with high rates of remission. SUMMARY: IgG4-ROD is an emerging cause of scleritis and uveitis and should be considered in any patient with multisystem inflammatory disease. New targeted immune therapies may improve outcomes and lead to clinical remission.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.201
GPT teacher head0.478
Teacher spread0.278 · 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

Citations61
Published2017
Admission routes1
Has abstractyes

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