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Record W4280538986 · doi:10.1177/13524585221093789

Serum MOG-IgG in children meeting multiple sclerosis diagnostic criteria

2022· article· en· W4280538986 on OpenAlexaff
Giulia Fadda, Patrick Waters, Mark Woodhall, Robert A. Brown, Julia O’Mahony, Denise Castro, Giulia Longoni, E. Ann Yeh, Ruth Ann Marrie, Douglas L. Arnold, Brenda Banwell, Amit Bar‐Or

Bibliographic record

VenueMultiple Sclerosis Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of ManitobaQueen's UniversityMontreal Neurological Institute and HospitalSickKids FoundationUniversity of TorontoMcGill UniversityHospital for Sick Children
Fundersnot available
KeywordsMultiple sclerosisMedicineImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is now recognized as distinct from multiple sclerosis (MS). OBJECTIVE: To evaluate the importance of considering myelin oligodendrocyte glycoprotein (MOG)-immunoglobulin-G (IgG) serology when applying MS diagnostic criteria in children. METHODS: Within a prospective cohort of children meeting MS criteria (median follow-up = 6 years, interquartile range (IQR) = 4-9), we measured MOG-IgG in serial archived serum obtained from presentation, and compared imaging and clinical features between seropositive and seronegative participants. RESULTS: Of 65 children meeting MS criteria (median age = 14.0 years, IQR = 10.9-15.1), 12 (18%) had MOG-IgG at disease onset. Seropositive participants were younger, had brain magnetic resonance imaging (MRI) features atypical for MS, rarely had cerebrospinal fluid (CSF) oligoclonal bands (2/8, 25%), and accumulated fewer T2 lesions over time. On serial samples, 5/12 (42%) were persistently seropositive, 5/12 (42%) became seronegative, and 2/12 (17%) had fluctuating results. All 12 children experienced a disease course different from typical MS. CONCLUSION: While children with MOG-IgG can have clinical, CSF, and MRI features conforming to MS criteria, the presence of MOG-IgG is associated with atypical features and predicts a non-MS disease course. Given MOG-IgG seropositivity can wane over time, testing at first attack is of considerable importance for the diagnosis of MOGAD.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.091
GPT teacher head0.298
Teacher spread0.207 · 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 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

Citations24
Published2022
Admission routes1
Has abstractyes

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