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Record W4285194572 · doi:10.4103/aian.aian_170_22

A Practical Approach to the Diagnosis and Management of Optic Neuritis

2022· article· en· W4285194572 on OpenAlexaff
Étienne Bénard-Séguin, Fiona Costello

Bibliographic record

VenueAnnals of Indian Academy of Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineOptic neuritisNeuromyelitis opticaMultiple sclerosisMyelin oligodendrocyte glycoproteinDiseaseDermatologyOptic neuropathySpectrum disorderOptic nervePediatricsPathologyImmunologyOphthalmologyPsychiatry

Abstract

fetched live from OpenAlex

Optic neuritis (ON), as an umbrella term, refers to a spectrum of inflammatory optic neuropathies arising from a myriad of potential causes. In its most common form, "typical" ON presents as a unilateral, painful subacute vision loss event in young Caucasian women. The Optic Neuritis Treatment Trial (ONTT) has historically guided our treatment of ON, and taught us important lessons about the clinical presentation, visual prognosis, and future risk of multiple sclerosis (MS) diagnosis associated with this condition. However, in the decades since the ONTT, several immune-mediated conditions such as neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte glycoprotein IgG associated disease (MOGAD) have been discovered, complicating the clinical approach to ON. Unlike MS, other central nervous system (CNS) inflammatory conditions are associated with ON subtypes that are potentially blinding, and prone to recurrence. Owing to differences in the clinical presentation, serological biomarkers, radiological findings, and prognostic implications associated with MS ON, NMOSD ON, and MOGAD ON subtypes, it is imperative that clinicians be aware of the diagnostic approach and management options for these conditions.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.720
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.221
GPT teacher head0.421
Teacher spread0.200 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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