Seizure control does not predict hippocampal subfield volume change in children with focal drug-resistant epilepsy
Bibliographic record
Abstract
Background and purpose Recurrent seizures have been reported to induce neuronal loss in the hippocampus. It is unclear whether seizure control influences hippocampal volume. The aims of this study were to determine if there was a change in total or subfield hippocampal volume over time in children with focal drug-resistant epilepsy, and whether seizure control influenced total or subfield hippocampal volumes. Methods Using FreeSurfer’s automated segmentation of brain magnetic resonance imaging scans, we calculated the total and subfield (including CA1, CA3, CA4, subiculum, presubiculum, parasubiculum, molecular layer and dentate gyrus) hippocampal volumes of children with non-lesional focal epilepsy. Seizure frequency and hippocampal volumes were assessed at baseline and follow-up. Patients were classified into those who were seizure free or have improvement in seizures (group 1) and those with no improvement in seizures (group 2) at follow-up. Results Thirty-seven patients were included, with mean age 10.31 ± 3.68 years at baseline. The interval between the two magnetic resonance imaging scans was 2.59 ± 1.25 years. There was no significant difference in the total and subfield hippocampal volumes for the whole cohort at follow-up compared to baseline (all P > 0.002). Seizure control of the two groups did not predict total or subfield hippocampal volume, after controlling for baseline volume, age, severity of seizure frequency at baseline and time interval between the magnetic resonance imaging scans (all P > 0.002). Conclusion We have found that total and subfield hippocampal volumes did not change, and seizure control did not predict hippocampal volumes at follow-up in children with drug-resistant 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".