Long-term full-scale intelligent quotient outcomes following pediatric and childhood epilepsy surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Cognitive measures are an important primary outcome of pediatric, adolescents, and childhood epilepsy surgery. The purpose of this systematic review and meta-analysis is to assess whether there are long-term alterations (≥ 5 years) in the Full-Scale Intelligence Quotient (FSIQ) of pediatric patients undergoing epilepsy surgery. METHODS: Electronic databases (EMBASE, MEDLINE, and Scopus) were searched for English articles from inception to October 2022 that examined intelligence outcomes in pediatric epilepsy surgery patients. Inclusion criteria were defined as the patient sample size of ≥ 5, average follow- up of ≥5 years, and surgeries performed on individuals ≤ 18 years old at the time of surgery. Exclusion criteria consisted of palliative surgery, animal studies, and studies not reporting surgical or FSIQ outcomes. Publication bias was assessed using a funnel plot and the Quality in Prognosis Studies (QUIPS) toolset was used for quality appraisal of the selected articles. A random-effects network meta-analysis was performed to compare FSIQ between surgical patients at baseline and follow-up and Mean Difference (MD) was used to calculate the effect size of each study. Point estimates for effects and 95% confidence intervals for moderation analysis were performed on variables putatively associated with the effect size. RESULTS: 21,408 studies were screened for abstract and title. Of these, 797 fit our inclusion and exclusion criteria and proceeded to full-text screening. Overall, seven studies met our requirements and were selected. Quantitative analysis was performed on these studies (N = 330). The mean long-term difference between pre- and post- operative FSIQ scores across all studies was noted at 3.36 [95% CI: (0.14, 6.57), p = 0.04, I2 = 0%] and heterogeneity was low. CONCLUSION: To our knowledge, this is the first meta-analysis to measure the long-term impacts of FSIQ in pediatric and adolescent epilepsy patients. Our overall results in this meta-analysis indicate that while most studies do not show long-term FSIQ deterioration in pediatric patients who underwent epilepsy surgery, there was an increase of 3.36 FSIQ points, however, the observed changes were not clinically significant. Moreover, at the individual patient level analysis, while most children did not show long-term FSIQ deterioration, few had significant decline. These findings indicate the importance of surgery as a viable option for pediatric patients with medically refractory epilepsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".