Long‐Term Valproate and Lamotrigine Treatment May Be a Marker for Reduced Growth and Bone Mass in Children with Epilepsy
Bibliographic record
Abstract
PURPOSE: To determine whether long-term treatment with valproate (VPA) and/or lamotrigine (LTG) in children with epilepsy is associated with altered growth and/or bone metabolism. METHODS: Twenty-seven boys and 26 girls, aged 3 to 17 years (9.2 +/- 3.9, mean +/- SD), with epilepsy treated with VPA and/or LTG for > or =2 years were evaluated for growth, nutrient intakes, physical activity, bone mineral density (BMD), and blood biochemical indices of mineral and bone metabolism. RESULTS: Twenty-three (43.4%) of the children had a body height below the 10th percentile. Z-scores for BMD below -1.5 occurred in 24.4% of the children. When patients were divided into two groups according to daily activity score, a significantly lower Z-score for total body BMD (p = 0.007), percentile for body height (p = 0.05), and plasma parathyroid hormone (PTH; p = 0.04), osteocalcin (p = 0.04) and 25-hydroxyvitamin D (25OHD) (p = 0.01) were found in the inactive compared with the active group. Z-score for total body BMD was correlated with daily activity score (r = 0.43, p = 0.008). Plasma intact osteocalcin and intact PTH values correlated significantly (r = 0.36, p = 0.02). Plasma 1,25-dihydroxyvitamin D was within normal range for all subjects. When patients were divided into LTG-alone, VPA-alone, and LTG-plus-VPA treatment groups, significantly lower (p < 0.05) plasma osteocalcin and percentile for body height were found in the VPA-plus-LTG treatment group. CONCLUSIONS: Long-term VPA and LTG therapy, particularly when combined, is associated with short stature, low BMD, and reduced bone formation. These alterations may be mediated primarily through reduced physical activity rather than through a direct link to the VPA and/or LTG therapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".