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Record W2972444901 · doi:10.1097/icu.0000000000000614

Clinical and radiologic approach to ‘typical’ versus antibody-related optic neuritis

2019· review· en· W2972444901 on OpenAlexaff
Marilou Caron‐Cantin, Dean M. Cestari, Élizabeth Fortin

Bibliographic record

VenueCurrent Opinion in Ophthalmology · 2019
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsOptic neuritisMedicineNeuromyelitis opticaOptic neuropathyMyelin oligodendrocyte glycoproteinMultiple sclerosisSpectrum disorderPathologyDermatologyOptic nerveOphthalmologyImmunologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Optic neuritis is an autoimmune optic neuropathy that has been associated with multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and more recently antimyelin oligodendrocyte glycoprotein (anti-MOG)-positive disorder. At initial presentation, it is often difficult to differentiate these entities given their significant overlap in clinical presentation and MRI findings. This review summarizes the distinguishing clinical and radiological features of MS, NMOSD, and anti-MOG disorders to help clinicians accurately diagnose and manage patients affected by these conditions. RECENT FINDINGS: Antiaquaporin-4 (AQP4) and more recently anti-MOG antibodies are both associated with central nervous system demyelinating diseases that often initially present with optic neuritis. Serologic testing now allows for a new classification of these overlapping conditions that can help to differentiate 'typical' optic neuritis that is often associated with MS from 'atypical' optic neuritis associated with NMOSD and anti-MOG-positive disorder. SUMMARY: Optic neuritis associated with MS, NMOSD, and anti-MOG-positive disease can have a similar clinical presentation. However, some clinical and radiologic findings can help clinicians to differentiate these entities so that they can be properly managed to optimize visual 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 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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.460
GPT teacher head0.553
Teacher spread0.093 · 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 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

Citations16
Published2019
Admission routes1
Has abstractyes

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