<b>Exploring the Effect of Vitamin B12 on Febrile Seizures in Children: A Systematic Review and Meta-Analysis of Case-Control Studies</b>
Bibliographic record
Abstract
Background: Vitamin B12 (VB12) is a water-soluble vitamin, deficiency of which causes an extensive heterogeneous spectrum of neurological symptoms including vision disturbances, paresthesia, tremor, and seizure. The aim of this investigation is to determine the effect of serum VB12 levels on pediatric patients with febrile seizure (FS). Methods: In this meta-analysis, case-control studies that evaluated the effect of serum VB12 levels in pediatric patients with FS were included. Web of Science, PubMed, Scopus, and Google Scholar were searched until August 13, 2024. The PICO criteria for this meta-analysis were as follows: Population/Patients (P: pediatric patients with febrile seizures); Issue of interest (I: serum levels of VB12); Comparison (C: control); Outcome (O: occurrence of febrile seizure). Quality assessment was assessed according to the Newcastle-Ottawa Scale (NOS) tool for case-control studies. The outcome assessment scales, study groups, and serum VB12 levels were extracted. Results: Of 435 initial articles, eventually 6 studies remained in the meta-analysis. Existing evidence indicated that serum VB12 concentrations were insignificantly lower in FS patients than controls (WMD= -1.09 pg/ml; 95% CI: -2.23, 0.04; P= 0.06), although a significant between-study heterogeneity was observed (I2= 98.10%, P< 0.001). Conclusion: The results of our study pointed out that there is low serum VB12 concentrations in FS patients compared with controls. Despite the fact, one of the best ways to prevent FS in children can be VB12 supplementation and proper diet 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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".