Laser Ablation of Periventricular Nodular Heterotopia for Medically Refractory Epilepsy
Bibliographic record
Abstract
Abstract Objective Periventricular Nodular Heterotopia (PVNH) is the most common neuronal heterotopia, frequently resulting in pharmaco-resistant epilepsy. PVNH has a deep location which renders localization of seizure onsets and traditional surgical therapy challenging and of limited success. Here we characterize variables that predict good epilepsy outcomes following surgical intervention using SEEG-informed MRgLITT. Methods A prospectively compiled surgical epilepsy database from a single high-volume epilepsy referral center was used to identify patients who underwent SEEG evaluation for PVNH and characterize the intervention on outcomes. Results Thirty-nine patients underwent SEEG-informed MRgLITT. Associated imaging abnormalities— mesial temporal sclerosis (MTS) or polymicrogyria (PMG) were treated based on SEEG. SEEG-guided MRgLITT of the seizure onset zone (SoZ) in PVNH and associated epileptic tissue was carried out. PVNH and PMG were densely sampled—mean 16.5(SD=2)/209.4(SD=36.9) SEEG probes/recording contacts. A single trajectory was used in 18, two in 13, and three or more in eight patients. Volumetric analyses revealed a high percentage of PVNH SoZ ablation (96.6%, SD=5.3%) in unilateral and bilateral (92.9%, SD=7.2%) cases. Mean follow-up duration was 31.4 months (SD=20.9). Seizure freedom was excellent overall: unilateral PVNH without other imaging abnormalities—80%; PVNH with MTS or PMG—63%; Bilateral PVNH—50%. SoZ ablation percentage significantly impacted surgical outcomes ( p <0.001). Interpretation PVNH plays a central role in seizure genesis. MRgLITT represents a transformative technological advance in PVNH-associated epilepsy with seizure control outcomes consistent with those seen in focal lesional epilepsies. In localized unilateral cases and otherwise normal imaging, performing PVNH ablation without invasive recordings may be reasonable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".