Lamotrigine Therapy of Epilepsy with Angelman's Syndrome
Bibliographic record
Abstract
PURPOSE: Angelman syndrome (AS) is a neurogenetic disorder characterized by developmental delay and a frequently refractory epileptic condition. Valproate, clonazepam and/or phenytoin are said to be the most effective antiepileptic drugs (AEDs) against the seizures in AS. Experience with the newer AEDs is very limited despite their better safety profile and tolerability. Considering its favorable side effect profile and its effectiveness against both partial and generalized seizures, we hypothesized that lamotrigine (LTG) might be more efficacious and better tolerated. METHODS: Potential patients for this retrospective study were identified from the epilepsy clinics at Notre-Dame, Sainte-Justine, and Yale New Haven hospitals. Patients were included in the study if they had AS along with refractory seizures. The medical record of each patient was reviewed with interest on seizure types, previous AEDs and response to LTG. RESULTS: Five patients (2M, 3F) were included in this study. Age at LTG ranged from 10 to 33 years old. All had >or=2 seizure types, mainly generalized tonic-clonic, myoclonic seizures, and atypical absences. Previously tried AEDs included valproic acid (5), benzodiazepines (5), phenytoin (4), carbamazepine (3), and topiramate (1). One patient had pancreatitis on phenytoin, one had worsened seizures on carbamazepine, and one developed hepatic encephalopathy on valproic acid. Three patients became seizure-free with LTG (9, 20, and 36 months FU), one was seizure-free for 1 year with subsequent loss of efficacy, and one showed >50% reduction in myoclonic seizures (20 months FU). No side effects were reported. CONCLUSION: LTG can be efficacious and well tolerated in patients with AS.
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 | 0.000 | 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) | 0.001 | 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".