Repeat testing enhances long-term verbal memory in children with epilepsy
Bibliographic record
Abstract
To (i) determine whether accelerated long-term forgetting (ALF) can be found using standardized verbal memory test materials in children with genetic generalized epilepsy (GGE) and temporal lobe epilepsy (TLE), and (ii) to establish whether ALF is impacted by executive skills and repeat testing over long delays. One hundred and twenty-three children aged 8 to 16, (28 with GGE, 23 with TLE, and 72 typically developing; TD) completed a battery of standardized tests assessing executive functioning and memory for two stories. Stories were recalled immediately and after a 30-min delay. To examine whether repeat testing impacts long-term forgetting, one story was tested via free recall at 1-day and 2-weeks, and the other at 2-weeks only. Recognition was then tested for both stories at 2-weeks. Children with epilepsy recalled fewer story details, both immediately and after 30-min relative to TD children. Compared to TD children, the GGE group, but not the TLE group, showed ALF, having significantly poorer recall of the story tested only at the longest delay. Poor executive skills were significantly correlated with ALF for children with epilepsy. Standard story memory materials can detect ALF in children with epilepsy when administered over long delays. Our findings suggest that (i) ALF is related to poor executive skills in children with epilepsy, and (ii) repeated testing may ameliorate ALF in some children.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".