Neurophysiological seizure-onset predictors of epilepsy surgery outcome: a multivariable analysis
Bibliographic record
Abstract
OBJECTIVE: The authors sought to determine which neurophysiological seizure-onset features seen during scalp electroencephalography (EEG) and intracerebral EEG (iEEG) monitoring are predictors of postoperative outcome in a large series of patients with drug-resistant focal epilepsy who underwent resective surgery. METHODS: The authors retrospectively analyzed the records of 75 consecutive patients with focal epilepsy, who first underwent scalp EEG and then iEEG (stereo-EEG) for presurgical assessment and who went on to undergo resective surgery between 2004 and 2015. To determine the independent prognostic factors from the neurophysiological scalp EEG and iEEG seizure-onset information, univariate and standard multivariable logistic regression analyses were used. Since scalp EEG and iEEG data were recorded at different times, the authors matched scalp seizures with intracerebral seizures for each patient using strict criteria. RESULTS: A total of 3057 seizures were assessed. Forty-eight percent (36/75) of patients had a favorable outcome (Engel class I-II) after a minimum follow-up of at least 1 year. According to univariate analysis, a localized scalp EEG seizure onset (p < 0.001), a multilobar intracerebral seizure-onset zone (SOZ) (p < 0.001), and an extended SOZ (p = 0.001) were significantly associated with surgical outcome. According to multivariable analysis, the following two independent factors were found: 1) the ability of scalp EEG to localize the seizure onset was a predictor of a favorable postoperative outcome (OR 6.073, 95% CI 2.011-18.339, p = 0.001), and 2) a multilobar SOZ was a predictor of an unfavorable outcome (OR 0.076, 95% CI 0.009-0.663, p = 0.020). CONCLUSIONS: The study findings show that localization at scalp seizure onset and a multilobar SOZ were strong predictors of surgical outcome. These predictors can help to select the better candidates for resective surgery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".