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Record W2098707698 · doi:10.1109/iembs.2005.1616573

Intracranial Electrode Visualization in Invasive Pre-surgical Evaluation for Epilepsy

2005· article· en· W2098707698 on OpenAlexaff
Yunhua Wang, Rajkumar Agarwal, Dang Khoa Nguyen, V. Domocos, Jean Gotman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalHôpital Notre-Dame
Fundersnot available
KeywordsEpilepsyElectroencephalographyVisualizationSurgical planningEpilepsy surgeryComputer scienceBrain stimulationMedicineNeuroscienceRadiologyStimulationArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Invasive intracranial electroencephalography (EEG) studies help identify the epileptogenic focus and assess if the identified zone overlies eloquent cortex by means of cortical stimulation. Proper interpretation and use of the intracranial recording/stimulation studies requires an effective display of multimodal information. We developed a software system which can combine the patient's segmented brain, post-implanting MRI/CT images, intracranial EEG findings and results from cortical stimulations into one integrated environment of three-dimensional visualization. This platform can efficiently facilitate the epilepsy pre-surgical evaluation and epilepsy surgery planning.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.357
Teacher spread0.337 · 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 designOther design
Domainnot available
GenreMethods

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

Citations8
Published2005
Admission routes1
Has abstractyes

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