Reexamining the effects of epilepsy surgery on IQ in children: Use of regression-based change scores
Bibliographic record
Abstract
Prior studies have found no adverse effects of pediatric epilepsy surgery on IQ. However, empirical techniques such as regression models, designed to account for confounding factors such as practice effects and test-retest reliability and able to provide a standardized method for evaluating outcome, have not been used in studying change after pediatric epilepsy. The goal of this study was to demonstrate the regression technique while empirically measuring the effect of epilepsy surgery on IQ in a group of pediatric patients. Predictors of retest IQ (e.g., baseline IQ, retest interval, demographics, epilepsy severity) were evaluated in a control group with intractable seizures (N = 23) assessed twice with the WISC-III. The resulting equation was used to evaluate IQ changes in a second group of children who underwent epilepsy surgery (N = 22). In controls, baseline IQ was a strong predictor of retest IQ. Number of AEDs was inversely related to retest IQ. Based on the control regression, four children (18%) in the surgical sample obtained significantly higher than expected postsurgical IQ scores and one child (5%) obtained a lower than expected IQ score. This study demonstrates that regression-based techniques yield informative estimates on outcome and may be an improvement over prior methods of measuring change after pediatric epilepsy surgery.
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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".