Effect of Gene Mutation on Seizures in Surgery for Tuberous Sclerosis Complex
Bibliographic record
Abstract
BACKGROUND: Tuberous sclerosis complex (TSC) is a rare genetic disorder that commonly leads to drug-resistant epilepsy in affected patients. This study aimed to determine whether the underlying genetic mutation (TSC1 vs. TSC2) predicts seizure outcomes following surgical treatments for epilepsy. METHODS: We retrospectively assessed TSC patients using the TSC Natural History Database core registry. Data review focused on outcomes in patients treated with surgical resection or vagus nerve stimulation. RESULTS: A total of 42 patients with a TSC1 mutation, and 145 patients with a TSC2 mutation, were identified. We observed a distinct clinical phenotype: children with TSC2 mutations tended to be diagnosed with TSC at a younger age than those with a TSC1 mutation (p < 0.001), were more likely to have infantile spasms (p < 0.001), and to get to surgery at a later age (p = 0.003). Among this TSC2 cohort, seizure control following resective epilepsy surgery was achieved in less than half (47%) the study sample. In contrast, patients with TSC1 mutations tended to have more favorable postsurgical outcomes; seizure control was achieved in 66% of this group. CONCLUSION: TSC2 mutations result in a more severe epilepsy phenotype that is also less responsive to resective surgery. It is important to consider this distinct clinical disposition when counseling families preoperatively with respect to seizure freedom. Larger samples are required to better characterize the independent effects of genetic mutation, infantile spasms, and duration of epilepsy as they relate to seizure control following resective or neuromodulatory epilepsy surgery.
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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 | 0.003 |
| 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".