P.036 Vagal nerve stimulation in three cases of continuous spike and wave in slow-wave sleep
Bibliographic record
Abstract
Background: Continuous spike and waves during slow-wave sleep (CSWS) is a childhood-onset epileptic encephalopathy that is characterized by clinical seizures, electrical status epilepticus during sleep (ESES), and neurocognitive regression. Early intervention can preserve neurocognitive development, and vagus nerve stimulator (VNS) therapy had positive outcomes in the few previously reported case reports. We present three patients with intractable CSWS unresponsive to medications, who had a positive response to VNS therapy. Methods: Review of clinical records of three pediatric patients diagnosed with CSWS were compared for selected clinical outcomes and electrographic data both prior to and in the years following the initiation of VNS therapy. Results: Three patients now aged 13, 16 and 20 years, were treated with VNS following intolerance and a lack of response to multiple medications (5-9) for CSWS. The ketogenic diet was not an option. The CSWS resolved in all three patients, resulting in improved cognitive function. Patient 3 had resurgence of CSWS on EEG when the VNS settings inadvertently reset to the factory settings and improved with adjustment in the cycling. Conclusions: In patients who are unresponsive to medication, VNS provides an alternative option for resolving CSWS to preserve and, in some cases, potentially restore neurocognitive function.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".