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Record W4298086760 · doi:10.1002/epi4.12654

Long‐term outcomes after <scp>NORSE</scp>: Treatment with vagus nerve stimulation

2022· article· en· W4298086760 on OpenAlexaff
Poul H. Espino, Jorge G. Burneo, Gaby Moscol, Teneille Gofton, Keith W. MacDougall, Ana Suller Martí

Bibliographic record

VenueEpilepsia Open · 2022
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsWestern University
Fundersnot available
KeywordsVagus nerve stimulationStimulationTerm (time)Vagus nerveMedicineNeuroscienceInternal medicinePsychologyPhysics

Abstract

fetched live from OpenAlex

New-onset refractory status epilepticus (NORSE) is associated with high mortality, therapy-resistant epilepsy (TRE), and poor cognitive and functional outcomes. Some patients develop multifocal TRE, for whom surgery with a curative intention, is not an option. In these patients, vagus nerve stimulation (VNS) is performed as a palliative treatment. We report the long-term outcomes regarding seizure frequency, functional and cognitive outcome, and effectiveness of VNS in two patients with TRE as a consequence of NORSE. In the first patient with cryptogenic NORSE, VNS implantation occurred during the acute stage, probably contributing to the cessation of her status epilepticus. However, in the long-term follow-up, the patient persisted with daily multifocal seizures. In the second patient, VNS implantation was delayed to manage his epilepsy when the NORSE, ultimately due to autoimmune encephalitis, had resolved. During long-term follow-up, no reduction in seizure frequency was achieved. This evidence supporting the use of VNS in patients with TRE after NORSE warrants further investigation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.082
GPT teacher head0.359
Teacher spread0.277 · 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 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

Citations21
Published2022
Admission routes1
Has abstractyes

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