Long‐term intellectual and developmental outcomes after pediatric epilepsy surgery: A systematic review and meta‐analysis
Bibliographic record
Abstract
In addition to the primary aim of seizure freedom, a key secondary aim of pediatric epilepsy surgery is to stabilize and, potentially, optimize cognitive development. Although the efficacy of surgical treatment for seizure control has been established, the long-term intellectual and developmental trajectories are yet to be delineated. We conducted a systematic review and meta-analysis of studies reporting pre- and postsurgical intelligence or developmental quotients (IQ/DQ) of children with focal lesional epilepsy aged ≤18 years at epilepsy surgery and assessed at >2 years after surgery. We determined the IQ/DQ change and conducted a random-effects meta-analysis and meta-regression to assess its determinants. We included 15 studies reporting on 341 patients. The weighted mean age at surgery was 7.1 years (range = .3-13.8). The weighted mean postsurgical follow-up duration was 5.6 years (range = 2.7-12.8). The overall estimate of the mean presurgical IQ/DQ was 60 (95% confidence interval [CI] = 47-73), the postsurgical IQ/DQ was 61 (95% CI = 48-73), and the change was +.94 IQ/DQ (95% CI = -1.70 to 3.58, p = .486). Children with presurgical IQ/DQ ≥ 70 showed a tendency for higher gains than those with presurgical IQ/DQ < 70 (p = .059). Higher gains were determined by cessation of antiseizure medication (ASM; p = .041), not just seizure freedom. Our findings indicate, on average, stabilization of intellectual and developmental functioning at long-term follow-up after epilepsy surgery. Once seizure freedom has been achieved, ASM cessation enables the optimization of intellectual and developmental trajectories in affected children.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".