An 18-year follow-up of seizure outcome after surgery for temporal lobe epilepsy and hippocampal sclerosis
Bibliographic record
Abstract
OBJECTIVES: To evaluate the very long-term clinical outcome of surgery for mesial temporal lobe epilepsy and unilateral hippocampal sclerosis (MTLE/HS) without atypical features. The impact of surgical technique and postoperative reduction of medication on this outcome was investigated. DESIGN: Prospective longitudinal cohort follow-up study for up to18 years. SETTING: Epilepsy surgery centre in a university hospital. PATIENTS: 108 patients who underwent unilateral MTLE/HS. INTERVENTION: Surgery for MTLE/HS. MAIN OUTCOME MEASURE: Engel classification (I). Clinical evaluations were based on systematic interviews in person or by phone. Kaplan-Maier survival curves estimated the probability of remaining seizure free. The impact of medication management in the postoperative outcome was analysed using Cox regression. RESULTS: The probability of remaining completely seizure-free at 12 and 18 years after MTLE/HS surgery was 65% and 62%, respectively. The risk of having any recurrence was 22% during the first 24 months and increased 1.4% per year afterwards. Type of surgical technique (selective amygdalohippocampectomy vs anterior temporal lobectomy) did not impact on outcome. Remaining on antiepileptic drugs and history of generalised clonic seizure diminished the probability of remaining seizure free. CONCLUSIONS: MTLE/HS surgery is able to keep patients seizure free for almost up to two decades. Removal of the neocortex besides the mesial portion of the temporal lobe does not lead to better chances of seizure control. These findings are applicable to the typical unilateral MTLE/HS syndrome and cannot be generalised for all types of TLE. Future longitudinal randomised controlled studies are needed to replicate these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".