Nonalcoholic Fatty Liver Disease, Carotid Intima-Media Thickness and Lipid Profile in Epileptic Children
Bibliographic record
Abstract
Background : Sodium valporate and carbamazepine are among frequent medications utilized for seizure control in children. Several adverse effects such as fatty liver disease, lipid profile changes and increased Intima-Media Thickness (IMT) were reported among cases who were treated with these medications. Methods : Here we assessed 38 children (under 18) who were treated by sodium valporate and carbamazepine for at least six months for developing adverse effects including fatty liver disease, lipid profile changes and increased IMT. Cases who were treated with two or more antiepileptic-drugs or treated less than six months with each of drugs were excluded. Fasting venous blood sample drawn andradiologic evaluation of liver and both carotid arteries by two independent individuals performed. Results : We found fatty liver disease in five patients who were treated with sodium valporate and lower White Blood Cell (WBC) count in carbamazepine group. Lipid profiles and IMT of both carotid arteries were not significantly different between groups. Conclusion : Children who are treated with sodium valproate would be better to be carefully assessed by sonographic modalities for developing fatty liver as an adverse effect of the drug. doi:10.4021/jnr40w
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".