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Record W4392293402 · doi:10.1002/epd2.20212

Acute encephalopathy with biphasic seizures and late reduced diffusion with concurrent transverse myelitis

2024· article· en· W4392293402 on OpenAlexaff
Amanda Austin‐Keiller, Christine Saint‐Martin, Kenneth A. Myers

Bibliographic record

VenueEpileptic Disorders · 2024
Typearticle
Languageen
FieldMedicine
TopicInfectious Encephalopathies and Encephalitis
Canadian institutionsMontreal Children's HospitalMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineAcute Transverse MyelitisMethylprednisoloneTransverse myelitisMultiple sclerosisMyelin oligodendrocyte glycoproteinAnesthesiaPathologyMyelinInternal medicineCentral nervous systemImmunology

Abstract

fetched live from OpenAlex

We describe a patient with acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) with unique features, including concurrent transverse myelitis. A 2-year-old previously healthy girl had clinical findings consistent with AESD, occurring in association with influenza A infection. The posterior brain regions were most severely affected, resulting in cortical blindness. She also developed bilateral limb weakness, and spine MRI revealed transverse myelitis in the cervical region. She was treated acutely with intravenous methylprednisolone. Serum anti-myelin oligodendrocyte glycoprotein and anti-aquaporin-4 antibodies were negative, as was an anti-extractable nuclear antigen panel. Although her clinical presentation was severe, she improved dramatically over the following months, and 6 months following initial presentation, her parents felt she had returned to baseline. This is the first report of AESD occurring in combination with transverse myelitis. The co-occurrence of the two conditions is unlikely to be coincidental, suggesting that there may be a shared or overlapping immunological pathway involved. The patient's recovery was impressive, which could partially relate to the acute treatment with corticosteroids.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.225
Teacher spread0.221 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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