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Lateralized Postictal EEG Delta Predicts the Side of Seizure Surgery in Temporal Lobe Epilepsy

2001· article· en· W2095350003 on OpenAlexaff
Mohammed M. Jan, Mark Sadler, Susan R. Rahey

Bibliographic record

VenueEpilepsia · 2001
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsElectroencephalographyTemporal lobeEpilepsyIctalPsychologyEpilepsy surgeryConcordanceAnterior temporal lobectomyAnesthesiaAudiologyMedicineNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The concordance of lateralized EEG postictal polymorphic delta activity (PPDA) to the side of seizure origin in temporal lobe epilepsy (TLE) has received limited study. Our objective was to study the lateralizing value of PPDA in patients with documented TLE. METHODS: A cohort of consecutive adults with TLE, detailed presurgical evaluation before temporal lobectomy, and minimal follow-up of 2 years were included. One author masked the ictal rhythm of presurgical EEGs and randomly presented 20 s of preictal and the postictal EEG to two electroencephalographers who were blind to all clinical data. They independently assigned PPDA to one of three categories: not present, bilateral, or lateralized (defined as newly appearing or an amplitude >50% of the preictal record). RESULTS: Eighty seizures from 29 patients were studied. Fifteen patients had a left, and 14 had a right temporal lobectomy. Twenty-three patients were seizure free or substantially improved (defined as simple partial or nocturnal seizures only). Lateralized PPDA was present in 64% of all EEGs and at least one record from 22 (76%) patients. Lateralized PPDA, when present, was concordant with the side of surgery in 96% of the EEGs. CONCLUSIONS: Lateralized PPDA is highly predictive of the side of ultimate temporal lobectomy, and by inference the side of seizure origin.

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)0.001
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 score0.008
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.027
GPT teacher head0.293
Teacher spread0.266 · 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

Citations43
Published2001
Admission routes1
Has abstractyes

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