Reoperation after failed resective epilepsy surgery in children
Bibliographic record
Abstract
OBJECTIVE Although epilepsy surgery is an effective treatment option, at least 20%-40% of patients can continue to experience uncontrolled seizures resulting from incomplete resection of the lesion, epileptogenic zone, or secondary epileptogenesis. Reoperation could eliminate or improve seizures. Authors of this study evaluated outcomes following reoperation in a pediatric population. METHODS A retrospective single-center analysis of all patients who had undergone resective epilepsy surgery in the period from 2001 to 2013 was performed. After excluding children who had repeat hemispherotomy, there were 24 children who had undergone a second surgery and 2 children who had undergone a third surgery. All patients underwent MRI and video electroencephalography (VEEG) and 21 underwent magnetoencephalography (MEG) prior to reoperation. RESULTS The mean age at the first and second surgery was 7.66 (SD 4.11) and 10.67 (SD 4.02) years, respectively. The time between operations ranged from 0.03 to 9 years. At reoperation, 8 patients underwent extended cortical resection; 8, lobectomy; 5, lesionectomy; and 3, functional hemispherotomy. One year after reoperation, 58% of the children were completely seizure free (International League Against Epilepsy [ILAE] Class 1) and 75% had a reduction in seizures (ILAE Classes 1-4). Patients with MEG clustered dipoles were more likely to be seizure free than to have persistent seizures (71% vs 40%, p = 0.08). CONCLUSIONS Reoperation in children with recurrent seizures after the first epilepsy surgery could result in favorable seizure outcomes. Those with residual lesion after the first surgery should undergo complete resection of the lesion to improve seizure outcome. In addition to MRI and VEEG, MEG should be considered as part of the reevaluation prior to reoperation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".