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Record W2058386552 · doi:10.1177/0883073813488666

Perfusion Imaging of Focal Cortical Dysplasia Using Arterial Spin Labeling

2013· article· en· W2058386552 on OpenAlexaff
Pia Wintermark, Mirna Lechpammer, Simon K. Warfield, Béla Kosaras, Masanori Takeoka, Annapurna Poduri, Joseph R. Madsen, Ann M. Bergin, Stephen Whalen, Frances E. Jensen

Bibliographic record

VenueJournal of Child Neurology · 2013
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsMcGill UniversityMontreal Children's Hospital
FundersNational Center for Research ResourcesU.S. National Library of MedicineNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeNational Institutes of Health
KeywordsCortical dysplasiaMicrovesselPerfusionDysplasiaEpilepsyMagnetic resonance imagingPerfusion scanningMedicinePathologyCortex (anatomy)RadiologyNeuroscienceImmunohistochemistryBiology

Abstract

fetched live from OpenAlex

Focal cortical dysplasia is the most common malformation of cortical development, causing intractable epilepsy. This study investigated the relationship between brain perfusion and microvessel density in 7 children with focal cortical dysplasia. The authors analyzed brain perfusion measurements obtained by magnetic resonance imaging of 2 of the children and the microvessel density of brain tissue specimens obtained by epilepsy surgery on all of the children. Brain perfusion was approximately 2 times higher in the area of focal cortical dysplasia compared to the contralateral side. The microvessel density was nearly double in the area of focal cortical dysplasia compared to the surrounding cortex that did not have morphological abnormalities. These findings suggest that hyperperfusion can be related to increased microvessel density in focal cortical dysplasia rather than only to seizures. Further investigations are needed to determine the relationship between brain perfusion, microvessel density, and seizure activity.

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: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.402

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 integrity0.0000.001
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 head0.015
GPT teacher head0.299
Teacher spread0.284 · 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

Citations36
Published2013
Admission routes1
Has abstractyes

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