Academic Outcomes in Individuals With Childhood-Onset Epilepsy: Mediating Effects of Working Memory
Bibliographic record
Abstract
OBJECTIVES: Academic difficulties are common in children with epilepsy, although little is known about the effect of various seizure-related and cognitive variables. Given that persistent seizures may negatively impact academics, and that working memory is predictive of academic abilities, we examined the effects of recent seizures and working memory on word reading, spelling, and arithmetic in pediatric epilepsy. We hypothesized that persistent seizures would be associated with lower working memory ability, which would in turn result in poorer academic performance. METHODS: Our sample consisted of 91 children with epilepsy being treated at the Hospital for Sick Children in Toronto, Canada, who underwent neuropsychological testing between 2002 and 2009 to help determine surgical candidacy. Four to 11 years later, follow-up testing was conducted on both surgical (n=61) and non-surgical (n=30) patients. Seizure status was defined by the presence or absence of seizures within the preceding 12 months. RESULTS: 5000 bias-corrected bootstrap resamples with replacement were used to calculate the 95% confidence intervals (CIs) for the indirect effect of seizure status on academics through working memory, controlling for baseline academic functioning. Persistent seizures were associated with reduced working memory, which was in turn associated with lower reading (B=-4.64, 95% CI [-10.21, -1.30]), spelling (B=-7.09, 95% CI [-13.97, -2.56], and arithmetic scores (B=-8.04, 95% CI [-13.66, -3.58] at follow-up. CONCLUSIONS: For children with intractable epilepsy, working memory deficits present a significant barrier to the development of academic skills. Working memory interventions may be a helpful adjunct to academic remediation in this population to facilitate academic progress. (JINS, 2017, 23, 594-604).
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".