Valproate associated brain volume‐loss in pediatric epilepsy—A case series
Bibliographic record
Abstract
Brain atrophy associated with valproate therapy is known from single case reports and is frequently accompanied by cognitive deterioration. We present a case series of incidental findings of brain volume loss in children treated with valproate and employed automatic brain volumetry to assess the effect size of volume loss. 3D T1w datasets were automatically segmented into white matter, gray matter, and cerebrospinal fluid using the SPM-12 algorithm. Respective volumes of cerebrum and cerebellum were read out and normalized to the total intracranial volume. We identified six patients (median age 148.5 [85-178] months) who had received valproate for a median time of 5 (2-23) months prior to MRI in which a loss of brain volume was noted. None had reported the occurrence of new clinical symptoms. Volumetry showed a volume loss of up to 28% for cerebral GM, 25% for cerebellar GM, 10% for cerebral WM, and 20% for cerebellar WM. A volume loss of >5% in at least one of the subvolumes was found in all patients, with the more prominent volume loss in the cerebrum and in gray matter. In one patient, post-valproate MRI was available and showed normalization of brain volume. Our case series indicates that valproate therapy might be associated with an asymptomatic volume loss of brain parenchyma in children with epilepsy and that this volume loss is assessable with automatic volumetry.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".