Effectiveness of Selenium Supplement on Cognitive Function in Patients with Epilepsy
Bibliographic record
Abstract
Objective: There is much evidence about the development of cognitive problems from the early stages of the epilepsy. Selenium can be beneficial for cognitive function because of its lowering effect on oxidative stress. This study was aimed to evaluate the effect of selenium on the cognitive performance of patients with epilepsy. Methods: Seventy patients between 20 and 65 years old with idiopathic generalized tonic-clonic epilepsy were enrolled with a simple randomized single-blind method and divided into case and control groups. The cognitive evaluation was performed by the Montreal Cognitive Assessment (MoCA) test at the beginning of study and 2 and 4 months after the treatment. The case group and control group received selenium and placebo, respectively. Results: At 2 months, MoCA test average score was 24.49 in the placebo group and 24.91 in the case group. At the end of the fourth month, the MoCA test average score was 24.54 in the placebo group and 26.31 in the case group. These findings did not demonstrate any significant difference between groups. Conclusion: The results of this study showed that selenium supplementation does not improve cognitive function in patients with epilepsy with mild cognitive impairment. Future studies with a longer course of trial and higher doses of selenium along with the measurement of serum selenium levels are recommended. Cite this article as: Maghbooli M, Davallou R, Jourahmad Z, Biglari S. Effectiveness of selenium supplement on cognitive function in patients with epilepsy. Neuropsychiatr Invest. 2022;60(2):38-41.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".