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Record W2138215136 · doi:10.1093/brain/awh446

Cortical triggers in generalized reflex seizures and epilepsies

2005· review· en· W2138215136 on OpenAlexaff
Edoardo Ferlazzo

Bibliographic record

VenueBrain · 2005
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsNeuroscienceReflex EpilepsyPsychologyEpilepsySensory systemCognitionReflexMyoclonic JerkElectroencephalography

Abstract

fetched live from OpenAlex

Activation of specific cortical territories by sensory stimuli or of less restricted areas of the brain by cognitive stimuli is known to induce apparently generalized seizures in predisposed patients; this is clinically and electroencephalographically distinct from reflex triggering of partial seizures. Photosensitive patients may have seizures when exposed to environmental stimuli producing appropriate flickering light or geometric patterns. Some children with benign myoclonic epilepsy in infancy have seizures triggered by unexpected touch or noise. Seizures induced by thinking have been reported in response to non-verbal higher mental activity such as mental arithmetic. Praxis-induced seizures are triggered by similar mental activities accompanied by the use of the hands. Language-induced seizures are usually triggered by verbal higher mental activity. Functional imaging and other methods have contributed to understanding how these seizures arise. Patients with these generalized reflex seizures appear to have regions of cortical hyperexcitability overlapping or coinciding with areas physiologically activated during specific sensory stimulations and cognitive or motor activities. When these areas receive appropriate afferent volleys and a critical mass of cortex is activated, an epileptic activity is produced that ultimately involves cortico-reticular or cortico-cortical pathways resulting in a generalized or bilateral epileptic event.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.102
GPT teacher head0.446
Teacher spread0.344 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations149
Published2005
Admission routes1
Has abstractyes

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