Epilepsy surgery in patients with bilateral temporal lobe seizures: A systematic review
Bibliographic record
Abstract
We explored the association between magnetic resonance imaging (MRI) lesion, degree of seizure laterality on intracranial electroencephalography (iEEG), and seizure outcome in patients with ambiguous or presumed bilateral temporal lobe epilepsy (BiTLE) on scalp EEG. We systematically reviewed the literature using Embase and MEDLINE up to May 31, 2012. Patients with bilateral iEEG, temporal lobe surgery, and follow-up ≥1 year were included. We undertook three separate analyses on patients whose scalp EEG showed ambiguous onset or BiTLE (1) group data of those whose iEEG demonstrated unilateral TLE, (2) group data of those whose iEEG demonstrated BiTLE, (3) individual patient analysis in those with BiTLE for whom iEEG seizure laterality data were provided. Of 1,403 patients with ambiguous or presumed BiTLE on scalp EEG, 1,027 (73%) proved to have unilateral TLE on iEEG and contributed to the first analysis. Of these, 58% had Engel class I and 9% Engel class II outcomes. Of 132 patients in the second analysis (true BiTLE), Engel class I and II outcomes were achieved in 23% and 14%, respectively. Of 41 patients in the third analysis, 66% and 2% had Engel class I and II outcomes, respectively. The median proportion of seizures ipsilateral to the resection on iEEG did not differ between BiTLE patients with Engel class I-II (76%) and Engel III-IV (78%) outcomes (p = 0.87). Patients with ambiguous or independent bitemporal seizure onset on scalp EEG achieved good surgical outcomes. Overall, a significantly higher proportion of patients achieved good outcomes when iEEG showed unilateral TLE (67%) than when it showed true BiTLE (45%). However, the degree of seizure lateralization in those with BiTLE was not associated with seizure outcome, and it has a limited role in selecting the side of 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".