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Record W4410052003 · doi:10.1002/epd2.70038

<scp>ILAE</scp> neuroimaging task force highlight: <scp>MRI</scp> detection of early life epilepsy caused by focal cortical dysplasia

2025· article· en· W4410052003 on OpenAlexaff
Nathan T. Cohen, L. Gilbert Vezina, Chima Oluigbo, Tayyba Anwar, John S. Archer, Boris C. Bernhardt, Lorenzo Caciagli, Fernando Cendes, Yotin Chinvarun, Luis Concha, Paolo Federico, Eliane Kobayashi, Godwin Ogbole, Stefan Rampp, Anna Elisabetta Vaudano, Irène Wang, Shuang Wang, Gavin P. Winston, William D. Gaillard

Bibliographic record

VenueEpileptic Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of CalgaryQueen's UniversityMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institutes of HealthNational Institute of Neurological Disorders and StrokeFundação de Amparo à Pesquisa do Estado de São PauloAmerican Academy of Neurology
KeywordsCortical dysplasiaNeuroimagingEpilepsyMedicineFunctional neuroimagingNeuroscienceEpilepsy surgeryPsychologyPsychiatry

Abstract

fetched live from OpenAlex

The ILAE Neuroimaging Task Force aims to publish educational case reports that highlight basic aspects of neuroimaging in epilepsy consistent with the ILAE's educational mission. Here, we describe a case series of three neonates with focal cortical dysplasia (FCD)-related drug-resistant epilepsy who underwent surgical intervention. The purpose of this series is to demonstrate the difficulties of identifying FCD in this age group and to highlight the potential added value of arterial spin labeling MRI for delineation of the epileptogenic zone. This series also supports the importance of the 2019 ILAE recommendations for structural imaging in epilepsy, the 2009 ILAE recommendations on imaging infants and children with recent-onset epilepsy, and the 2022 ILAE consensus classification of FCD.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2025
Admission routes1
Has abstractyes

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