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Record W4403839207 · doi:10.1016/j.seizure.2024.10.013

Expert opinion on use of vagus nerve stimulation therapy in the management of pediatric epilepsy: A Delphi consensus study

2024· article· en· W4403839207 on OpenAlexaff
James W. Wheless, Jeffrey S. Raskin, Anthony L. Fine, Kelly G. Knupp, John M. Schreiber, Adam P. Ostendorf, Gregory W. Albert, Eric H. Kossoff, Joseph R. Madsen, Prakash Kotagal, Adam L. Numis, Nisha Gadgil, Deborah Holder, Elizabeth A. Thiele, George M. Ibrahim

Bibliographic record

VenueSeizure · 2024
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsSickKids FoundationUniversity of Toronto
FundersLivaNova
KeywordsVagus nerve stimulationExpert opinionMedicineEpilepsyDelphi methodVagus nerveAnesthesiaStimulationIntensive care medicinePsychiatryComputer scienceInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

PURPOSE: To provide consensus-based recommendations for use of vagus nerve stimulation (VNS) therapy in the management of pediatric epilepsy. METHODS: Delphi methodology with two rounds of online survey was used to build consensus. A steering committee developed 43 statements related to pediatric epilepsy and the use of VNS therapy, which were evaluated by a panel of 12 neurologists/neurosurgeons with expertise in pediatric epilepsy, who graded their agreement with each statement on a scale of 1 ("I do not agree at all") to 5 ("I strongly agree"). For each statement, consensus was established if ≥70% of the agreement scores were 4 or 5 and <30% were 1 or 2 in the final survey. RESULTS: Twenty-four statements regarding the need for seizure reduction in pediatric epilepsy, the recommended treatment algorithm, the benefits and safety of VNS therapy, management of side effects of VNS therapy, patient selection for VNS therapy, and the use, dosing, and titration of VNS therapy achieved consensus. VNS and other neuromodulation therapies should be considered for pediatric patients with drug-resistant epilepsy who are not candidates for resective surgery, or who do not remain seizure free after resective surgery. When VNS therapy is initiated, the target dose range should be achieved via the fastest and safest titration schedule for each patient. Scheduled programming can be helpful in dose titration. CONCLUSION: The expert consensus statements represent the panelists' collective opinion on the best practice use of VNS therapy to optimize outcomes in the management of pediatric 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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.203
GPT teacher head0.411
Teacher spread0.209 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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