Outcome of lesional epilepsy surgery
Bibliographic record
Abstract
BACKGROUND: We investigated the utility of epilepsy surgery and postoperative outcome in patients with lesional epilepsy in Iran, a relatively resource-poor setting. METHODS: This prospective longitudinal study was conducted during 2007-2017 in Kashani Comprehensive Epilepsy Center, Isfahan, Iran. Patients with a diagnosis of intractable focal epilepsy, with MRI lesions, who underwent epilepsy surgery and were followed up ≥ 24 months, were included and evaluated for postoperative outcome. RESULTS: A total of 214 patients, with a mean age of 26.90 ± 9.82 years (59.8% men) were studied. Complex partial seizure was the most common type of seizure (85.9%), and 54.2% of the cases had auras. Temporal lobe lesions (75.2%) and mesial temporal sclerosis (48.1%) were the most frequent etiologies. With a mean follow-up of 62.17 ± 19.33 months, 81.8% of patients became seizure-free postoperatively. Anticonvulsants were reduced in 86% of the cases and discontinued in 40.7%. In keeping with previous studies, we found that seizure freedom rates were lower among patients with longer follow-up periods. CONCLUSIONS: We found high rates of seizure freedom after surgery in lesional epilepsy patients despite limited facilities and infrastructure; antiepileptic medications were successfully tapered in almost half of the patients. Considering the favorable outcome of epilepsy surgery in our series, we believe that it is a major treatment option, even in less resource-intensive settings, and should be encouraged. Strategies to allow larger scale utility of epilepsy surgery in such settings in the developing world and dissemination of such knowledge may be considered an urgent clinical need, given the established mortality and morbidity in refractory epilepsy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".