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Record W4408283974 · doi:10.1055/a-2538-3354

Disconnection Methods in the Surgical Treatment of Epilepsy

2025· review· en· W4408283974 on OpenAlexaff
Runze Yang, Goichiro Tamura, Julia Jacobs, Walter Hader

Bibliographic record

VenueSeminars in Neurology · 2025
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsDisconnectionCorpus callosotomyMedicineHemispherectomyQuadrant (abdomen)EpilepsyEpilepsy surgerySurgeryPopulationPsychiatry

Abstract

fetched live from OpenAlex

Disconnection procedures in epilepsy surgery have become an important tool for the management of multifocal drug-resistant epilepsy. In this chapter, we will review their indications, describe the technical procedures, and review outcome data in the literature. Among the curative approaches, anterior quadrant disconnection, posterior quadrant (PQ) disconnection, and functional hemispherectomy can be performed for patients whose epileptic focus resides in one hemisphere or one quadrant. Seizure freedom rates from these procedures range from 50 to 81% for anterior quadrant disconnections, 50 to 92% for PQ disconnections, and 43 to 93% for hemispherectomy. Although typically performed in the pediatric population, data suggest that carefully selected adult patients could also benefit from a disconnection procedure. Of the palliative approaches, corpus callosotomy has been shown to be effective for drop attacks, resulting in significant improvement in seizure frequency, severity, and quality of life. Minimally invasive alternatives to standard open corpus callosotomies with laser interstitial thermal therapy (LITT) have been proposed. Overall, surgical disconnection procedures are an effective way of treating multifocal epilepsy, with good outcomes that can improve the quality of life for these patients.

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: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.067
GPT teacher head0.472
Teacher spread0.404 · 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
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

Citations4
Published2025
Admission routes1
Has abstractyes

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