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Record W3081679482 · doi:10.1097/wno.0000000000001064

Vision Loss From Giant Cell Arteritis in Patients With Other Ocular Diagnoses

2020· article· en· W3081679482 on OpenAlexaff
Prem Nichani, Valérie Biousse, Nancy J. Newman, Jonathan A. Micieli

Bibliographic record

VenueJournal of Neuro-Ophthalmology · 2020
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGiant cell arteritisMedicineOptic neuritisAnterior ischemic optic neuropathyMedical diagnosisArteritisOptic neuropathyGlaucomaVasculitisIschemic optic neuropathyGold standard (test)Differential diagnosisSurgeryOphthalmologyOptic nerveRadiologyPathologyMultiple sclerosisDisease

Abstract

fetched live from OpenAlex

ABSTRACT: Vision problems from giant cell arteritis (GCA) can be difficult to diagnose as patients may present with vision loss in the absence of systemic symptoms, have other comorbidities that affect inflammatory blood markers, or have other ocular diagnoses. We present 3 cases illustrating this point including a patient with advanced glaucoma with worsening vision from posterior ischemic optic neuropathy from GCA, a patient with arteritic anterior ischemic optic neuropathy (AAION) erroneously diagnosed as optic neuritis without elevated inflammatory blood markers due to corticosteroid use, and a patient with AAION and a history of nonarteritic anterior ischemic optic neuropathy in her fellow eye and untreated obstructive sleep apnea. GCA should be kept in the differential diagnosis for patients over 50 years of age even if they carry other ocular diagnoses. Temporal artery biopsy remains the gold standard for GCA diagnosis and is often required in equivocal cases.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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