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Record W3093277595 · doi:10.1684/epd.2020.1202

ILAE Neuroimaging Task Force highlight: Review MRI scans with semiology in mind

2020· article· en· W3093277595 on OpenAlexaff
Paolo Federico, Denise Ng, Andrea Bernasconi, Boris C. Bernhardt, Hal Blumenfeld, Fernando Cendes, Yotin Chinvarun, Graeme D. Jackson, Victoria L. Morgan, Stefan Rampp, Anna Elisabetta Vaudano, Irène Wang

Bibliographic record

VenueEpileptic Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityHotchkiss Brain InstituteOntario Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsSemiologyNeuroimagingIctalMedicineEpilepsy surgeryRadiologyEpilepsyMagnetic resonance imagingLesionPsychologySurgeryPsychiatry

Abstract

fetched live from OpenAlex

The ILAE Neuroimaging Task Force aims to publish educational case reports highlighting basic aspects related to neuroimaging in epilepsy consistent with the educational mission of the ILAE. It is important to obtain MRI scans early in the clinical course of epilepsy, using an optimized protocol. Furthermore, it is critical that MRI scans are reviewed by experts who have been provided with all the clinical information and results from other investigations. We report a patient with a 21-year history of drug-resistant seizures who was admitted from another centre for presurgical evaluation. She had four previous MRI scans from this centre which were reported as unremarkable. However, a review of the MRI scan obtained on the day of admission, with the patient's ictal semiology in mind, resulted in identification of an epileptogenic lesion which was later confirmed by video-EEG monitoring and interictal PET. This lesion was present on all previous MRI scans and showed no change. The patient underwent lesionectomy, and histopathology of the resected specimen was consistent with a dysembryoplastic neuroepithelial tumour. The patient remains seizure-free, 2.5 years after surgery. This case highlights the importance of obtaining detailed descriptions of seizure semiology and considering them when reviewing MR images.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.546
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.016
GPT teacher head0.283
Teacher spread0.267 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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