Clobazam as Add-on Therapy for Temporal Lobe Epilepsy and Hippocampal Sclerosis
Bibliographic record
Abstract
BACKGROUND: Clobazam is a benzodiazepine with known antiepileptic action; however, it is not considered first line therapy in the treatment of epilepsy. The objective of this study was to evaluate the efficacy of clobazam as add-on therapy in adults with temporal lobe epilepsy associated with MRI evidence of hippocampal sclerosis (HS). METHOD: This is a retrospective study, conducted at our epilepsy clinic which evaluated clobazam as add-on therapy in patients with temporal lobe epilepsy and MRI signs of HS. Clobazam was prescribed based on the minimum effective dose up to the maximum tolerated dose. RESULTS: Seventy-eight patients met the inclusion criteria (51 women), ages ranging from 16 to 76 years old (mean=42.2). Dosage of clobazam ranged from 5 to 60 mg/day (mean=22.6 mg/day). Clobazam was used from one month to eight years (mean=29 months). Sixteen (20.5%) patients were seizure-free, 20 (25.5%) had more than 75% improvement in seizure control, eight (10%) had more than 50% and 20 (26%) were non responders to clobazam. In 14 (18%) we could not determine seizure frequency during follow-up. The improvement in seizure control lasted for more than one year in 30 (68%) patients. CONCLUSION: Our data suggest that clobazam should be considered as add-on therapy in the treatment of patients with temporal lobe epilepsy associated with MRI signs of HS.
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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 | 0.001 | 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".