Surgically Remediable Secondary Network Epileptic Encephalopathies With Continuous Spike Wave in Sleep: Lesions May Not Be Visible on Brain Magnetic Resonance Imaging (MRI)
Bibliographic record
Abstract
Background Continuous spike wave in sleep (CSWS) is an electroencephalogram (EEG) pattern associated with developmental and epileptic encephalopathy with spike-and-wave activation in sleep (DEE-SWAS). This etiologically heterogeneous syndrome may occur because of genetic factors and congenital or acquired brain lesions. We studied the pattern of clinical presentation and underlying etiologies in patients with DEE-SWAS that respond to resective surgery. Methods We reviewed our clinical and research databases for patients who had resolution of CSWS following surgical resection of a focal lesion. Results We identified 5 patients meeting inclusion criteria. In 3 of 5, an epileptogenic structural abnormality was not apparent on brain magnetic resonance imaging (MRI). In all 3 patients, focal cortical dysplasia was identified through intracranial EEG monitoring. Significance DEE-SWAS may be a secondary bilateral network epilepsy syndrome, which can be treated with resection of the inciting focal lesion. In patients with drug-resistant CSWS, clinicians should consider a complete epilepsy presurgical workup, including intracranial EEG monitoring.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".