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Record W2045978424 · doi:10.1212/wnl.0000000000001203

Eosinophilic CNS vasculitis can mimic demyelinating disease of the brain and spinal cord

2015· article· en· W2045978424 on OpenAlexaff
Raphaël Schneider, Jenny P. Tsai, David G. Muñoz, Daniel Selchen

Bibliographic record

VenueNeurology · 2015
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineVasculitisSpinal cordPathologyBrain biopsyEosinophilicBiopsyDisease

Abstract

fetched live from OpenAlex

A 61-year-old man developed progressive ascending sensory loss to T8 and paraparesis over 4 weeks. MRI revealed ovoid T2-hyperintense lesions in juxtacortical and periventricular areas, and in the T7-T10 spinal segment, with varying degrees of peripheral gadolinium enhancement (figure 1). He received 5 days of high-dose corticosteroids and plasmapheresis for presumed demyelination, without clinical response. Brain biopsy showed vasculitis involving small arteries with transmural inflammatory cell infiltrates including numerous eosinophils (figure 2). Eosinophilic vasculitis can cause ischemic strokes1; it rarely involves the spinal cord.2 Our case illustrates that it should be considered in the differential diagnosis of CNS demyelination.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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