Sodium valproate combined with levetiracetam in pediatric epilepsy and its influence on NSE, IL‑6, hs‑CRP and electroencephalogram improvement
Bibliographic record
Abstract
Efficacy of sodium valproate combined with levetiracetam (LEV) in pediatric epilepsy and its influence on neuron-specific enolase (NSE), interleukin-6 (IL-6) and high-sensitivity C-reactive protein (hs-CRP) as well as electroencephalogram (EEG) improvement were studied. Patients (n=100) with pediatric epilepsy admitted to and treated in Xiantao First People's Hospital Affiliated to Yangtze University from December 2015 to 2018 were enrolled in this study and randomly divided into observation group (n=50) and control group (n=50). Sodium valproate was administered in the control group, and the treatment with LEV was combined with sodium valproate in the observation group. After 12 weeks the cognitive function of patients was assessed using the Mini-Mental State Examination (MMSE) scale, Montreal cognitive assessment (MoCA) scale and Wechsler Memory Scale-Revised in China (WMS-RC). The quality of life (QOL) of patients was evaluated with the QOL in epilepsy-31 inventory (QOLIE-31) scale and Barthel Index, and blood was drawn from the patients to detect the neurological function indicators [NSE and glial fibrillary acidic protein (GFAP)] and inflammatory indicators (IL-6, IL-2 and hs-CRP). After treatment, the incidence rates of adverse reactions notably declined in the observation group (P<0.05), and the improvement in the cognitive function in the observation group were both superior to those in the control group (P<0.05). Observation group had lowered content of NSE, GFAP, IL-6, hs-CRP and IL-2 (P<0.05), and α wave was markedly decreased, but θ and δ waves were notably increased in the observation group (P<0.05). In the treatment of pediatric epilepsy, sodium valproate combined with LEV produces better efficacy, fewer adverse reactions, significantly improves patients' QOL and notably lowers the content of NSE, IL-6 and hs-CRP with notable EEG improvement, so it is a safe and reliable treatment that is worth popularization.
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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 | 0.000 | 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 | 0.000 |
| 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".