Fertility treatments and stroke: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Fertility therapies are becoming an increasingly common option for conception, but there is little knowledge on the interactions of assisted reproductive technology (ARTs) and stroke in women, and its use among women with a history of stroke. This scoping review aims to examine the current state of knowledge and identify the knowledge gaps regarding: (1) the stroke risk with use of fertility therapy and (2) the safety of fertility therapy among stroke survivors, to help guide future research and clinical practice. DESIGN: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist for scoping reviews was followed. DATA SOURCES: MEDLINE (Ovid), Cochrane Central Registry of Controlled Trials (Ovid), EMBASE (Ovid), Medline, ClinicalTrials.gov and International Clinical Trials Registry Platform were searched through 20 June 2023. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: All observational studies pertaining to use of fertility and stroke in humans over the age of 18 were included in this review. DATA EXTRACTION AND SYNTHESIS: Title and abstract and subsequent full-text review were performed by two independent reviewers in duplicate. A narrative synthesis of final articles for data abstraction is presented. RESULTS: 10 studies met inclusion criteria. The only meta-analysis that was included could not estimate the risk of stroke among all individuals undergoing fertility therapy due to lack of high-quality prospective cohort studies. There was only one prospective cohort that examined pregnancy among stroke survivors. Among the 32 pregnancies, one was conceived through in vitro fertilisation (IVF), although the details regarding stroke type, time since stroke, medications and IVF protocol were not specified. Therefore, the safety and effectiveness of ART among stroke survivors is currently unknown. CONCLUSION: In this comprehensive review of existing fertility therapy science and stroke risk, we identify a large knowledge gap and a major scientific need for a systematic approach and prospective studies to better inform the risk of stroke during fertility therapy, especially among stroke survivors. Understanding this risk and developing treatment strategies serves to better inform women and healthcare providers as more women with a history of stroke seek and contemplate fertility therapy. TRIAL REGISTRATION NUMBER: DOI 10.17605/OSF.IO/PHQ4X.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".