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Record W4387692265 · doi:10.1097/icu.0000000000001007

Evidence-based management of optic neuritis

2023· article· en· W4387692265 on OpenAlexaff
Émilie Bergeron, Marc A. Bouffard

Bibliographic record

VenueCurrent Opinion in Ophthalmology · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversité LavalHôpital du Saint-Sacrement
Fundersnot available
KeywordsOptic neuritisMedicineMultiple sclerosisNeuromyelitis opticaMyelin oligodendrocyte glycoproteinDermatologyPlasmapheresisPediatricsImmunologyAntibody

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Optic neuritis can result from several distinct causes, including multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody disease (MOGAD), when not idiopathic. This review discusses evidence-based treatment approaches contingent upon each specific cause of optic neuritis. RECENT FINDINGS: Current evidence highlights the need for prompt plasmapheresis as adjunct to intravenous methylprednisolone (IVMP) in patients with NMOSD-associated optic neuritis. Recent advances have included a proliferation of novel disease modifying therapies (DMTs) for long-term management of NMOSD and an understanding of how existing therapeutic options can be leveraged to optimally treat MOGAD. SUMMARY: In acute idiopathic or MS-associated optic neuritis, IVMP hastens visual recovery, though it does not substantially affect final visual outcomes. IVMP and adjunctive plasmapheresis are beneficial in the treatment of NMOSD-associated optic neuritis, with a shorter time-to-treatment associated with a higher likelihood of recovery. The natural history of untreated MOGAD-associated optic neuritis is unclear but treatment with IVMP is near-universal given phenotypic similarities with NMOSD. Long-term immunosuppressive therapy is warranted in patients with NMOSD as well as in patients with MOGAD with poor visual recovery or recurrent attacks.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.364
GPT teacher head0.469
Teacher spread0.105 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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