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Record W2113370192 · doi:10.1051/medsci/20031910999

Bases cellulaires des transitions de l’état de sommeil aux paroxysmes épileptiformes

2003· review· fr· W2113370192 on OpenAlexaff
Mircea Steriade, Florin Amzica, Igor Timofeev

Bibliographic record

Venuemédecine/sciences · 2003
Typereview
Languagefr
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEpilepsyUnconsciousnessNeuroscienceSleep (system call)Slow-wave sleepCortex (anatomy)Cerebral cortexElectroencephalographyPsychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Epileptic seizures mainly develop during slow-wave sleep. Our experiments, using multi-site, extra- and intracellular recordings, show a transformation without discontinuity from sleep patterns to seizures. The cerebral cortex is the minimal substrate of paroxysms with spike-wave complexes at ~3 Hz. Simultaneously, thalamocortical neurons are steadily inhibited and cannot relay signals from the outside world to cortex. This may explain the unconsciousness during certain types of 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.181
GPT teacher head0.395
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2003
Admission routes1
Has abstractyes

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