Bibliographic record
Abstract
A temporal resection in patients with periventricular nodular heterotopia (PNH) and intractable focal seizures yields poor results. To define the role of heterotopic grey matter tissue in epileptogenesis and to improve outcome, we performed stereoencephalography (SEEG) recordings in eight patients with uni- or bilateral PNH and intractable focal epilepsy. The SEEG studies aimed to evaluate the most epileptogenic areas and included the allo- and neocortex and at least one nodule of grey matter. Interictal spiking activity was found in ectopic grey matter in three patients, in the cortex overlying the nodules in five and in the mesial temporal structures in all. At least one heterotopion was involved at seizure onset in six patients, synchronous with the overlying neocortex or ipsilateral hippocampus. Two patients had their seizures originating in the mesial temporal structures only. Six patients had surgery and the resected areas included the seizure onset, with follow-up from 1 to 8 years. An amygdalo-hippocampectomy was performed in two (Engel class Id and III), an amygdalo-hippocampectomy plus removal of an adjacent heterotopion in two (class Ia), and a resection of two contiguous nodules plus a small rim of overlying occipital cortex in one patient (class Id). One patient with bilateral PNH had three adjacent nodules resected and an ipsilateral amygdalo-hippocampectomy resulting in a reduction of the number of seizures by 25-50%. The best predictor of surgical outcome is the presence of a focal epileptic generator; this generator may or may not include the PNH. Invasive recording is required in patients with PNH; it improves localization and is the key to better outcome.
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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 | 0.001 |
| 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".