Effects of antiseizure medications on sexual hormones and functions in males with epilepsy: A systematic review and meta‐analysis
Bibliographic record
Abstract
The fertility effects of antiseizure medications (ASMs) have been highlighted in females of reproductive age; however, the effects in males have not been extensively analyzed. This review aims to summarize the existing evidence of how ASMs affect sexual hormones and functions in males with epilepsy. We searched the Embase, PubMed, and MEDLINE databases in January 2024 to identify studies measuring sexual hormones, sexual function, or sperm parameters of males with epilepsy taking any ASM except valproic acid who were compared to a control group. A systematic review summarizing the effects of valproic acid on sexual function was published in 2018; therefore, we excluded valproic acid to avoid duplicating existing evidence. Risk of bias assessments were specific to the study type and included the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, NIH Quality Assessment Tool for Before-After (Pre-Post) Studies, and Cochrane Risk of Bias-2 tool for randomized trials. The systematic review included 32 studies, and the meta-analysis included 22 studies. Using random effect models, we calculated mean differences or rate ratios for studies assessing the association between ASMs and male sexual hormones or functions. Analyses were run for each combination of individual ASM or ASM characteristic, outcome, and comparison group. Males taking oxcarbazepine had significantly higher levels of testosterone, luteinizing hormone, and follicle-stimulating hormone compared to healthy controls. Conversely, there was no evidence of differences in any outcomes between levetiracetam or lamotrigine and comparison groups. Analyses that included untreated males with epilepsy rarely differed from males taking ASMs, highlighting the potential importance of epilepsy on altered sex hormones and functions. However, results should be interpreted cautiously, as many analyses included only a few studies and had high heterogeneity.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".