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Record W2132315084 · doi:10.1186/s12348-015-0047-y

IgG4-associated orbital and ocular inflammation

2015· article· en· W2132315084 on OpenAlexaff
Cecilia S. Lee, George J. Harocopos, Courtney L. Kraus, Aaron Lee, Gregory P. Van Stavern, Steven M. Couch, P. Kumar Rao

Bibliographic record

VenueJournal of Ophthalmic Inflammation and Infection · 2015
Typearticle
Languageen
FieldMedicine
TopicIgG4-Related and Inflammatory Diseases
Canadian institutionsUniversity of British Columbia
FundersNational Eye InstituteNational Institutes of Health
KeywordsMedicineInflammationOptic neuritisPathologyGranulomatosis with polyangiitisUveitisBiopsyOphthalmic pathologyDiseaseDermatologyVasculitisMultiple sclerosisImmunologyOphthalmologyNeuro-ophthalmologyGlaucoma

Abstract

fetched live from OpenAlex

BACKGROUND: IgG4-associated orbital and ocular inflammation is a relatively unknown entity characterized by sclerosing inflammation with infiltration of IgG4-positive plasma cells. Some so-called idiopathic inflammation syndromes are being re-classified as IgG4-associated inflammation with histopathologic evaluation. FINDINGS: We report three cases with differing manifestations of IgG4-associated ocular and orbital inflammation: a case of recurrent, treatment-refractory sclero-uveitis that was diagnosed as granulomatosis with polyangiitis with an IgG4-related component, a case of pachymeningitis with optic neuritis that resulted in permanent visual loss, and a case of orbital inflammatory pseudotumor. All three would have been incompletely diagnosed without thorough histopathologic evaluation (including immunohistochemistry). CONCLUSIONS: IgG4-associated disease is an idiopathic, multi-organ inflammatory state that can manifest as chronic, relapsing, sclerosing inflammation in virtually any organ system. There is a wide range of presentations in ocular and orbital inflammation. Ophthalmologists should keep IgG4-associated inflammation in mind when examining chronic, sclerofibrosing inflammation with multi-system involvement. The histology of biopsy specimens is crucial in making the correct diagnosis. Timely assessment may lead to fewer diagnostic tests and more targeted therapy.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.269
Teacher spread0.249 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations56
Published2015
Admission routes1
Has abstractyes

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