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Record W3048265729 · doi:10.1684/epd.2020.1174

MRI essentials in epileptology: a review from the ILAE Imaging Taskforce

2020· review· en· W3048265729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEpileptic Disorders · 2020
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsOntario Brain InstituteUniversity of CalgaryHotchkiss Brain InstituteMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsEpilepsyContext (archaeology)Magnetic resonance imagingMedicineDiffusion MRIMedical physicsReading (process)PsychologyRadiologyPsychiatry

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) plays a central role in the management and evaluation of patients with epilepsy. It is important that structural MRI scans are optimally acquired and carefully reviewed by trained experts within the context of all available clinical data. The aim of this review is to discuss the essentials of MRI that will be useful to health care providers specialized in epilepsy, as outlined by the competencies and learning objectives of the recently developed ILAE curriculum. This review contains information on basic MRI principles, sequences, field strengths and safety, when to perform and repeat an MRI, epilepsy MRI protocol (HARNESS-MRI) and the basic reading guidelines, and common epileptic pathologies. More advanced topics such as MRI-negative epilepsy, functional MRI and diffusion-weighted imaging are also briefly discussed. Although the available resources can differ markedly across different centers, it is the hope that this review can provide general guidance in the everyday practice of using MRI for patients with epilepsy.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.028
GPT teacher head0.362
Teacher spread0.334 · 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