MétaCan
Menu
← Back to cohort
Record W4220889713 · doi:10.21203/rs.3.rs-1350279/v1

Antibody-Positive Neuromyelitis Optica Spectrum Disorder After Second COVID-19 Vaccination: A Case Report.

2022· preprint· en· W4220889713 on OpenAlexaff
Stephanie Kuntz, Georges Saab, Raphaël Schneider

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeuromyelitis opticaSpectrum disorderCoronavirus disease 2019 (COVID-19)MedicineAntibodyVaccinationVirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakImmunologyInternal medicinePsychiatryOutbreak

Abstract

fetched live from OpenAlex

Abstract Background: We report a case of de novo aquaporin-4 positive neuromyelitis optica spectrum disorder following BNT162b SARS-CoV-2 vaccination.Case Presentation: An 80-year-old South Asian man presented two days following his second dose of the Pfizer-BioNTech COVID-19 mRNA BNT162b2 vaccine with progressive left-sided leg weakness and numbness resulting in falls. MRI of the spine revealed a longitudinally extensive transverse myelitis from T3-T4 to T9-T10. Serum antibody testing revealed positive aquaporin-4 (AQP4) antibodies. He was diagnosed with de novo AQP4 positive neuromyelitis optica spectrum disorder (NMOSD) and was treated with high dose intravenous methylprednisolone and plasma exchange with some improvement. He was subsequently treated with mycophenolate mofetil and a slow steroid wean.Conclusions: Evidence suggests vaccinations may trigger de novo NMOSD or NMOSD relapses in some individuals. Ongoing vaccine surveillance and research are needed to understand the risk of NMOSD post-COVID-19 vaccinations further.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.076
GPT teacher head0.454
Teacher spread0.378 · 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 designCase report
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueResearch Square→Same topicMultiple Sclerosis Research Studies→French-language works237,207→