A.06 Selective amygdalohippocampectomy in pediatric medically refractory temporal lobe epilepsy yields worse seizure outcomes
Bibliographic record
Abstract
Background: Selective amygdalohippocampectomy (SAH) is a surgical option in well-selected cases of pediatric medically refractory temporal lobe epilepsy (TLE). The objective of this study was to compare the surgical outcome and the rate of reoperation for ongoing or recurrent seizures between SAH and anterior temporal lobectomy (ATL) in pediatric TLE. Methods: Retrospective review of 78 pediatric intractable TLE patients referred to the Comprehensive Epilepsy Program at our institution between 1988 and 2015 treated initially with either a trans-middle temporal gyrus SAH (19) or ATL (59). Patients underwent baseline long-term video electroencephalography and 1.5-Tesla MRI. Neuropsychological testing was performed preoperatively and 12-months postoperatively (including reoperations). Results: The mean follow-up was 64 months (range, 12-186 months). The average age at initial surgery was 10.6±5 years with an average delay of 5.7±4 years between seizure onset and surgery. Ultimately 78% were seizure-free (61/78) at most recent follow-up. Seizure freedom after initial surgical treatment was achieved in 81% of patients who underwent ATL (48 patients) versus 42% in SAH (8 patients; p<0.001). Of patients with ongoing disabling seizures following SAH, reoperation (ATL) was offered in 8 resulting in seizure freedom in 63%, without interval neuropsychological decline. Conclusions: SAH amongst well-selected pediatric TLE results in significantly worse seizure control compared with ATL.
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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) | 0.002 | 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".