Expert opinion on use of vagus nerve stimulation therapy in the management of pediatric epilepsy: A Delphi consensus study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.215 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".