Changing Global Trends in Seizure Outcomes Following Resective Surgery for Tuberous Sclerosis in Children with Medically Intractable Epilepsy
Bibliographic record
Abstract
Introduction. Tuberous sclerosis (TS) is the leading cause of genetic epilepsy worldwide. Here, we evaluate changes in seizure outcomes following resective epilepsy surgery in children with TS over time. Methods. A systematic review of the literature was performed to identify studies reporting seizure outcomes following resective epilepsy surgery in children with TS. Using an individual participant meta-analysis approach, seizure outcomes and associated covariates were combined. Multivariate logistic regression was used to determine significant associations between seizure outcomes and time of surgery. Results. Twenty studies from 1966 to present, yielding 186 participants, met the inclusion criteria for the study. On univariate analysis, there was a significant improvement in seizure outcomes in children who underwent resective epilepsy surgery within the last 15 years compared to older cohorts (chi-square 4.1; P = 0.043). On multivariate analysis, adjusting for length of followup, this trend was not significant (OR 0.52; 95% CI 0.23-1.17; P = 0.11). In the last 15 years, a greater proportion of younger children also underwent resective surgery compared to older cohorts (OR 0.93; 95% CI 0.89-0.97; P < 0.01). Conclusions. A trend towards improved seizure outcomes following resective surgery for TS was observed from 1966 to present on multivariate analysis.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".