Seizure outcomes in children with Rasmussen’s encephalitis undergoing resective or hemispheric epilepsy surgery: an individual participant data meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to perform an individual participant data meta-analysis to identify preoperative factors associated with a good seizure outcome in children with Rasmussen's encephalitis (RE) undergoing resective or hemispheric epilepsy surgery. METHODS: Electronic databases (PubMed, Web of Science, CINAHL) were searched with no language or date restrictions to identify cohort studies of consecutive participants undergoing resective surgery that reported seizure outcomes. The authors recorded all preoperative factors that could plausibly be associated with seizure outcomes and used Cox regression analysis to identify which of these variables were associated with seizure freedom (i.e., Engel class I). RESULTS: Of 720 citations, 19 articles reporting on 187 participants were eligible. Seizure freedom (Engel class I) was observed in 113 participants (60.4%). On univariate analyses, younger age at disease onset (hazard ratio [HR] 0.906, p = 0.001), younger age at surgery (HR 0.928, p < 0.001), shorter time to surgery (HR 0.921, p = 0.001), and hemispherectomy (HR 0.283, p < 0.001) were all associated with longer time to postoperative seizure recurrence. Additionally, multivariable analysis including the aforementioned variables showed that younger age at surgery (HR 0.946, p = 0.043) and hemispherectomy (HR 0.297, p < 0.001) were independently and significantly associated with a greater time to seizure recurrence and longer duration of seizure freedom. CONCLUSIONS: The majority of pediatric patients undergoing resective or hemispheric surgery for RE achieve good seizure outcome. Although small retrospective cohort studies are inherently prone to bias, the best available evidence utilizing individual participant data suggests hemispheric surgery and younger age at surgery are associated with good seizure outcomes following epilepsy surgery. Large, multicenter observational studies with long-term follow-up are required to evaluate the risk factors identified in this review.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.046 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".