Pre-pregnancy fertility guidance for women of childbearing age with epilepsy: A scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Epilepsy is one of the most common neurological conditions affecting women of reproductive age. Epilepsy management during pregnancy is a clinical conundrum, requiring a balance between seizure control and risk minimization for women with epilepsy, as well as for their fetuses. Objective: In this review, we aimed to systematically search, evaluate, and summarize relevant evidence on perinatal fertility guidance for women with epilepsy to provide a basis for medical staff to offer comprehensive fertility counseling. Methods: Systematic searches were conducted for system evaluations, expert consensus, guidelines, and evidence summarizing best clinical practices and clinical decision-making in fertility guidance for women with epilepsy. The search encompassed resources from the National Institute of Health and Clinical Optimization in the United Kingdom, the National Guidelines Network in the United States, the International Guidelines Collaboration Network, Registered Nurses' Association of Ontario guidelines in Canada, the Scottish Interhospital Guidelines Network, the International Anti-Epilepsy Alliance, the Royal College of Obstetricians and Gynecologists in the United Kingdom, the American Association of Obstetricians and Gynecologists, Chinese Anti Epilepsy Association, PubMed, UpToDate, BMJ Best Clinical Practice, Web of Science, Embase, JBI Evidence Based Health Care Center, Cochrane Library Database, and China National Knowledge Infrastructure databases or websites from inception to July 31st 2023. Two researchers with evidence-based nursing backgrounds independently completed literature screening and quality evaluation while extracting and summarizing evidence based on themes. Results: A total of 11 articles were ultimately included, comprising one clinical decision, six guidelines, two expert consensus statements, one meta-analysis, and one evidence summary. In these articles, authors collectively addressed five themes: pre-pregnancy consultation and preparation, pregnancy management, delivery management, postpartum and newborn care, and selection of contraceptive measures. Conclusion: We have synthesized the most compelling evidence regarding reproductive counseling for women with epilepsy across the preconception, pregnancy, labor and delivery, and postpartum periods. This serves as a foundation for healthcare professionals to implement effective reproductive counseling practices. In clinical practice, medical personnel should consider the patient's clinical context, individual circumstances, and preferences when devising treatment and care plans. This will facilitate the implementation of scientifically-sound management strategies for women with epilepsy to enhance maternal and infant outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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) | 0.000 | 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 it