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Record W1978293872 · doi:10.1159/000235749

Patient with Postcentral Gyrectomy Demonstrates Reliable Localization of Hand Motor Area Using Magnetoencephalography

2009· article· en· W1978293872 on OpenAlexafffund
Elizabeth W. Pang, William Gaetz, James M. Drake, Samuel Strantzas, Matt J. MacDonald, Hiroshi Otsubo, O. Carter Snead

Bibliographic record

VenuePediatric Neurosurgery · 2009
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsHospital for Sick ChildrenSickKids Foundation
FundersHospital for Sick Children
KeywordsMagnetoencephalographyMedicinePostcentral gyrusSensory systemMotor cortexNeuroscienceMotor areaSensory cortexMagnetic resonance imagingStimulationRadiologyPsychologyElectroencephalography

Abstract

fetched live from OpenAlex

Magnetoencephalography (MEG) data analyzed with novel spatial filtering methods, namely event-related beamforming (ERB), have shown success in localizing hand motor areas in healthy adults and in a group of pediatric patients with peri-Rolandic tumors. The validity of this method to localize the primary motor field in a pediatric tumor case was confirmed by intraoperative direct cortical stimulation. Currently, the reliability of this method has not been demonstrated. We report on a 16-year-old boy with localization-related epilepsy originating from his right hemisphere sensory cortex. Hand motor and sensory areas were identified preoperatively by ERB analysis of MEG data. The patient underwent invasive monitoring which localized the epileptic focus to right postcentral gyrus, immediately posterior to the MEG motor area and adjacent to the MEG sensory area. The patient received a gyrectomy of sensory cortex guided by intraoperative direct cortical stimulation to ensure sparing of hand motor cortex. Replication of the MEG motor mapping protocol postoperatively demonstrated reliable localization of the motor and sensory areas. We also discuss caveats for future applications of this protocol.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.241
Teacher spread0.226 · 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

Citations10
Published2009
Admission routes2
Has abstractyes

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