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Assessment of Pediatric Optic Neuritis Visual Acuity Outcomes at 6 Months

2020· article· en· W3092906274 on OpenAlexaboutno aff
Stacy L. Pineles, Michael X. Repka, Grant T. Liu, Amy Waldman, Mark Borchert, Sangeeta Khanna, Gena Heidary, Jennifer Graves, Veeral S. Shah, Mark J. Kupersmith, Raymond T. Kraker, David K. Wallace, Susan A. Cotter, Jonathan M. Holmes

Bibliographic record

VenueJAMA Ophthalmology · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOptic neuritisVisual acuityPediatricsProspective cohort studyNeuromyelitis opticaOphthalmologyMultiple sclerosisSurgeryPsychiatry

Abstract

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Importance: Optic neuritis (ON) in children is uncommon. There are limited prospective data for visual acuity (VA) outcomes, associated diseases, and neuroimaging findings. Prospective data from a large sample would be useful for counseling families on treatment decisions and prognosis. Objective: To prospectively study children with a first episode of ON, describe VA after 6 months, and ascertain the network's (Pediatric Eye Disease Investigator Group and Neuro-Ophthalmology Research Disease Investigator Consortium) ability to enroll pediatric patients with ON prospectively. Design, Setting, and Participants: This nonrandomized cohort study was conducted from September 20, 2016, to July 20, 2018, at 23 sites in the United States and Canada in pediatric ophthalmology or neuro-ophthalmology clinics. A total of 44 children (aged 3-15 years) presented with a first episode of ON (visual loss, pain on eye movements, or both) within 2 weeks of symptom onset and at least 1 of the following in the affected eye: a distance high-contrast VA (HCVA) deficit of at least 0.2 logMAR below age-based norms, diminished color vision, abnormal visual field, or optic disc swelling. Exclusion criteria included preexisting ocular abnormalities or a previous episode of ON. Main Outcomes and Measures: Primary outcomes were monocular HCVA and low-contrast VA at 6 months. Secondary outcomes were neuroimaging, associated diagnoses, and antibodies for neuromyelitis optica and myelin oligodendrocyte glycoprotein. Results: A total of 44 children (mean age [SD], 10.2 [3.5] years; 26 boys [59%]; 23 White individuals [52%]; 54 eyes) were enrolled in the study. Sixteen patients (36%) had bilateral ON. Magnetic resonance imaging revealed white matter lesions in 23 children (52%). Of these children, 8 had myelin oligodendrocyte glycoprotein-associated demyelination (18%), 7 had acute disseminated encephalomyelitis (16%), 5 had multiple sclerosis (11%), and 3 had neuromyelitis optica (7%). The baseline mean HCVA was 0.95 logMAR (20/200), which improved by a mean 0.76 logMAR (95% CI, 0.54-0.99; range, -0.70 to 1.80) to 0.12 logMAR (20/25) at 6 months. The baseline mean distance low-contrast VA was 1.49 logMAR (20/640) and improved by a mean 0.72 logMAR (95% CI, 0.54-0.89; range, -0.20 to 1.50) to 0.73 logMAR (20/100) at 6 months. Baseline HCVA was worse in younger participants (aged <10 years) with associated neurologic autoimmune diagnoses, white matter lesions, and in those of non-White race and non-Hispanic ethnicity. The data did not suggest a statistically significant association between baseline factors and improvement in HCVA. Conclusions and Relevance: The study network did not reach its targeted enrollment of 100 pediatric patients with ON over 2 years. This indicates that future treatment trials may need to use different inclusion criteria or plan a longer enrollment period to account for the rarity of the disease. Despite poor VA at presentation, most children had marked improvement by 6 months. Associated neurologic autoimmune diagnoses were common. These findings can be used to counsel families about the disease.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.080
GPT teacher head0.405
Teacher spread0.325 · 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".

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Citations30
Published2020
Admission routes1
Has abstractyes

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