Elevated Blood-Based Brain Biomarker Levels in Patients with Epileptic Seizures: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Recently, growing attention has been paid to the changes of brain biomarkers following the epilepsy. However, establishing specific epilepsy-related biomarkers has been impeded due to contradictory findings. This study systematically reviewed the evidence on brain biomarkers in epilepsy and determined reliable biomarkers in epileptic patients. A comprehensive systematic search of online databases was performed to find eligible studies up to August 2019. The quality of studies methodologically was assessed using the Newcastle-Ottawa Scale score. Among the several biomarkers, S100 calcium binding protein B (S100B) and neuron specific enolase (NSE) have been qualified for meta-analysis of the association between epilepsy and the brain biomarkers. Inverse-variance weights method was used to calculate pooled standardized mean difference (SMD) estimate with 95% CI, and random effects meta-analysis was conducted taking into account conceptual heterogeneity. Sensitivity analysis and publication bias assessment was performed using Stata. Of 29 studies that were qualified for further analysis, only 22 studies were eligible to quantify by meta-analysis. Significant increase of serum S100B levels (SMD = 0.80; 95% CI 0.18 to 1.42) but not NSE (SMD = 0.45; 95% CI -0.09 to 1.00) has been found in epileptic patients compared with healthy controls. Subgroup meta-analysis by age demonstrated that S100B could be found in pediatric (SMD = 1.15; 95% CI 0.03 to 2.27) not adult patients (SMD = 0.43; 95% CI -0.12 to 0.98). Findings of this meta-analysis indicate that serum level of S100B is significantly increased in epileptic patients, suggesting the elevation and release of the brain biomarkers from brain to blood following epileptic seizures.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".