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Record W4401559113 · doi:10.1016/j.ophtha.2024.08.018

Radiologic Predictors of Visual Outcome in Myelin Oligodendrocyte Glycoprotein-Related Optic Neuritis

2024· article· en· W4401559113 on OpenAlexafffund
Armin Handzic, Jim Shenchu Xie, Nanthaya Tisavipat, Roisin M. O’Cearbhaill, Deena Tajfirouz, Kevin D. Chodnicki, Eoin P. Flanagan, John J. Chen, Jonathan A. Micieli, Edward Margolin

Bibliographic record

VenueOphthalmology · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsKensington HealthMcMaster UniversityUniversity of Toronto
FundersNational Institutes of HealthNational Institute of Neurological Disorders and StrokeUniversity of TorontoAmgen
KeywordsMedicineOptic neuritisMyelin oligodendrocyte glycoproteinRadiological weaponMyelinOphthalmologyOutcome (game theory)OligodendrocyteVisual systemNeuroscienceMultiple sclerosisPathologyRadiologyInternal medicineRetinaImmunologyCentral nervous system

Abstract

fetched live from OpenAlex

PURPOSE: This study aimed to determine whether magnetic resonance imaging (MRI) biomarkers are associated with visual prognosis in myelin oligodendrocyte protein (MOG)-associated optic neuritis (ON). DESIGN: Cross-sectional analysis. PARTICIPANTS: Patients meeting 2023 international diagnostic criteria for MOG antibody-associated disease who were seen for first episodes of MOG-associated ON at 3 tertiary neuro-ophthalmology practices between January 2017 and July 2023 were enrolled. Patients who received < 3 months of neuro-ophthalmic follow-up and did not demonstrate visual recovery (visual acuity [VA] ≥ 20/20 and visual field mean deviation [VFMD] > -5.0 dB) during this time were excluded. METHODS: Patients underwent contrast-enhanced, fat-suppressed MRI of the brain and orbits within 1 month of symptom onset. MAIN OUTCOME MEASURES: The associations between radiologic biomarkers and poor VA outcome (< 20/40), incomplete VA recovery (< 20/20), and poor VFMD outcome (VFMD < -5.0 dB) were assessed using multivariable logistic regression adjusting for time from symptom onset to treatment and nadir VA or VFMD. Radiologic biomarkers included length of optic nerve enhancement (> 25% vs. < 25%; > 50% vs. < 50%; and > 75% vs. < 75%); degree of orbital, canalicular, and intracranial or chiasmal optic nerve enhancement (mild vs. moderate to severe compared with the lacrimal gland); and absence versus presence of optic nerve sheath enhancement on baseline T1-weighted MRI. RESULTS: A total of 129 eyes of 92 patients (median age, 37.0 years [interquartile range, 20.8-51.3 years]; 65.2% female) were included. Poor VA outcome was seen in 6.2% of patients, incomplete VA recovery was seen in 19.4% of patients, and poor VFMD outcome was seen in 16.9% of patients. Compared with eyes with moderate to severe enhancement, eyes with mild orbital optic nerve enhancement were more likely to have poor VA outcome (odds ratio [OR], 8.57; 95% confidence interval [CI], 1.85-51.14; P = 0.009), incomplete VA recovery (OR, 7.31, 95% CI, 2.42-25.47; P = 0.001), and poor VFMD outcome (adjusting for time to treatment: OR, 6.81; 95% CI, 1.85-28.98; P = 0.005; adjusting for nadir VFMD: OR, 11.65; 95% CI, 1.60-240.09; P = 0.04). Lack of optic nerve sheath enhancement additionally was associated with incomplete VA recovery (OR, 3.86; 95% CI, 1.19-12.85; P = 0.02) compared with the presence of enhancement. These associations remained consistent in subgroup logistic regression analysis of MRIs performed before initiation of treatment but were not seen in pairwise analysis of MRIs performed after treatment. CONCLUSIONS: In eyes with first MOG-associated ON episodes, milder enhancement in the orbital optic nerve was associated with poorer VA and visual field recovery. Prospective and mechanistic studies are needed to confirm the prognostic usefulness of MRI in MOG-associated ON. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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)0.001
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 score0.070
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.366
Teacher spread0.321 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

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