Sex steroid hormones and epilepsy: Effects of hormonal replacement therapy on seizure frequency of postmenopausal women with epilepsy—A systematic review
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hormonal replacement therapy (HRT) is used for symptomatic treatment of menopause. Some evidence suggests a proconvulsant effect of estrogen and an anticonvulsant role of progesterone. Thus, the use of exogenous sex steroid hormones might influence the course of epilepsy in peri- and postmenopausal women with epilepsy (WWE). We conducted a systematic review on the impact of HRT on the frequency of seizures of WWE. METHODS: PubMed and Scopus were searched for articles published from inception until August 2022. Abstracts from the past 5 years from the European Academy of Neurology and European Epilepsy Congresses were also reviewed. Article reference lists were screened, and relevant articles were retrieved for consultation. Interventional and observational studies on WWE and animal models of estrogen deficiency were included. Critical appraisal was performed using the revised Cochrane risk-of-bias tool for randomized trials and ROBINS-E tool. RESULTS: Of 497 articles screened, 13 studies were included, including three human studies. One cross-sectional study showed a decrease in seizure frequency in WWE using combined HRT, a case-control study showed an increase in comparison with controls, and a randomized clinical trial found a dose-dependent increase in seizure frequency in women with focal epilepsy taking combined HRT. Ten studies addressing the impact of HRT in rat models were also included, which showed conflicting results. CONCLUSIONS: There is scarce evidence of the impact of HRT in WWE. Further studies should evaluate the harmful potential, and prospective registries are needed for monitoring this population.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".