Long-term seizure outcome after mesial temporal lobe epilepsy surgery: corticalamygdalohippocampectomy versus selective amygdalohippocampectomy
Bibliographic record
Abstract
OBJECT: Resection strategies for the treatment of temporal lobe epilepsy (TLE) are a matter of discussion, and little information is available. The aim of this study was to compare seizure outcomes at the 5-year follow-up in patients with medically refractory unilateral mesial TLE (MTLE) due to hippocampal sclerosis (HS) who were treated using a cortical amygdalohippocampectomy (CorAH) or a selective AH (SelAH). METHODS: The authors obtained data from 100 adult patients who underwent surgery for MTLE. Fifty patients underwent a CorAH and 50 underwent an SelAH. Seizure control achieved with each technique was compared using the Engel classification scheme. RESULTS: Overall, at the 5-year follow-up, favorable (Engel Classes I and II) seizure outcomes were noted in 82 and 90% of patients who had undergone CorAH and SelAH, respectively. Furthermore, 40% of the patients who had undergone a CorAH and 58% of those who had undergone an SelAH were seizure free (Engel Class Ia). There was no statistically significant difference between the 2 surgical approaches in terms of seizure outcome at the 5-year follow-up (p = 0.38). CONCLUSIONS: Both CorAH and SelAH can lead to similar favorable seizure control in patients with MTLE/HS. However, the authors suggest that the transcortical selective approach has the great advantage of minimizing or completely abolishing the impact of dividing several venous and arterial adhesions which are tedious, time consuming, and, at times, associated with some degree of cerebral swelling.
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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 | 0.001 |
| 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) | 0.000 | 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".