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Record W4306176950 · doi:10.1111/epi.17437

Seizures in <scp>anti‐Hu</scp>–associated extra‐limbic encephalitis: Characterization of a unique disease manifestation

2022· article· en· W4306176950 on OpenAlexaff
Adrian Budhram, Manas Sharma, G. Bryan Young

Bibliographic record

VenueEpilepsia · 2022
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Neurological Disorders and Treatments
Canadian institutionsSouth Bruce Grey Health CentreLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsLimbic encephalitisEpilepsyNeuroscienceMedicineContext (archaeology)IctalLimbic systemNeuroimagingPsychologyEncephalitisImmunologyCentral nervous systemBiology

Abstract

fetched live from OpenAlex

Anti-Hu-associated neurologic autoimmunity most often occurs in the context of small cell lung cancer and typically presents with peripheral neuropathy, cerebellar ataxia, and/or limbic encephalitis. Extra-limbic encephalitis causing seizures is a rare disease manifestation, with only sparse reports in the literature. Herein we present a patient with seizures in anti-Hu-associated extra-limbic encephalitis, and review the literature for other cases to more fully characterize this entity. Among 27 patients we identified, the median age was 46 years (range: 2-69 years) and 18 of 27 (67%) were female. Focal motor seizures were most common, followed by ictal expressive speech difficulty. Seizure semiologies along with neuroimaging findings most frequently suggested the involvement of the peri-Rolandic cortex, more anterior frontal operculum, and insula, although other cortical regions were rarely affected as well. In contrast to other classical paraneoplastic neurologic syndromes, good response to treatment with attainment of seizure-free survival was often reported, although over one-third still died. A propensity for chronic seizures among children indicated the potential to develop autoimmune-associated epilepsy. The predilection for certain extra-limbic regions, as well as the possibility of good response to treatment, may reflect unique disease mechanisms that would benefit from further study.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.501

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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 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

Citations14
Published2022
Admission routes1
Has abstractyes

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