Rare Genetic Variation and Outcome of Surgery for Mesial Temporal Lobe Epilepsy
Bibliographic record
Abstract
OBJECTIVE: Genetic factors have long been debated as a cause of failure of surgery for mesial temporal lobe epilepsy (MTLE). We investigated whether rare genetic variation influences seizure outcomes of MTLE surgery. METHODS: We performed an international, multicenter, whole exome sequencing study of patients who underwent surgery for drug-resistant, unilateral MTLE with normal magnetic resonance imaging (MRI) or MRI evidence of hippocampal sclerosis and ≥2-year postsurgical follow-up. Patients with either sustained seizure freedom (favorable outcome) or ongoing uncontrolled seizures since surgery (unfavorable outcome) were included. Exomes of controls without epilepsy were also included. Gene set burden analyses were carried out to identify genes with significant enrichment of rare deleterious variants in patients compared to controls. RESULTS: Nine centers from 3 continents contributed 206 patients operated for drug-resistant unilateral MTLE, of whom 196 (149 with favorable outcome and 47 with unfavorable outcome) were included after stringent quality control. Compared to 8,718 controls, MTLE cases carried a higher burden of ultrarare missense variants in constrained genes that are intolerant to loss-of-function (LoF) variants (odds ratio [OR] = 2.6, 95% confidence interval [CI] = 1.9-3.5, p = 1.3E-09) and in genes encoding voltage-gated cation channels (OR = 2.4, 95% CI = 1.4-3.8, p = 2.7E-04). Proportions of subjects with such variants were comparable between patients with favorable outcome and those with unfavorable outcome, with no significant between-group differences. INTERPRETATION: Rare variation contributes to the genetic architecture of MTLE, but does not appear to have a major role in failure of MTLE surgery. These findings can be incorporated into presurgical decision-making and counseling. ANN NEUROL 2022.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".