Lateralized Postictal EEG Delta Predicts the Side of Seizure Surgery in Temporal Lobe Epilepsy
Bibliographic record
Abstract
PURPOSE: The concordance of lateralized EEG postictal polymorphic delta activity (PPDA) to the side of seizure origin in temporal lobe epilepsy (TLE) has received limited study. Our objective was to study the lateralizing value of PPDA in patients with documented TLE. METHODS: A cohort of consecutive adults with TLE, detailed presurgical evaluation before temporal lobectomy, and minimal follow-up of 2 years were included. One author masked the ictal rhythm of presurgical EEGs and randomly presented 20 s of preictal and the postictal EEG to two electroencephalographers who were blind to all clinical data. They independently assigned PPDA to one of three categories: not present, bilateral, or lateralized (defined as newly appearing or an amplitude >50% of the preictal record). RESULTS: Eighty seizures from 29 patients were studied. Fifteen patients had a left, and 14 had a right temporal lobectomy. Twenty-three patients were seizure free or substantially improved (defined as simple partial or nocturnal seizures only). Lateralized PPDA was present in 64% of all EEGs and at least one record from 22 (76%) patients. Lateralized PPDA, when present, was concordant with the side of surgery in 96% of the EEGs. CONCLUSIONS: Lateralized PPDA is highly predictive of the side of ultimate temporal lobectomy, and by inference the side of seizure origin.
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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) | 0.000 | 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".