ArticleBMC pregnancy and childbirth2022
Prediction model of gonadotropin starting dose and its clinical application in controlled ovarian stimulation.
Article in BMC pregnancy and childbirth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review.Qatar medical journal · 2026Review
- Pharmacogenetic analysis using artificial intelligence (AI) to identify polymorphisms associated with sub-optimal ovarian response and hyper-response.Journal of assisted reproduction and genetics · 2025Observational
- Artificial Intelligence in Assisted Reproductive Technology: A New Era in Fertility Treatment.Cureus · 2025Review
- Clinical data-based modeling of IVF live birth outcome and its application.Reproductive biology and endocrinology : RB&E · 2024Article
- Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.Journal of medical Internet research · 2024Article
- Analysis of controlled ovarian hyperstimulation protocols in women over 35 years old with poor ovarian response: a real-world study.BMC pregnancy and childbirth · 2023Article
- Letrozole cotreatment improves the follicular output rate in high-body-mass-index women with polycystic ovary syndrome undergoing IVF treatment.Frontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundSelecting an appropriate and personalized Gn starting dose (GSD) is an essential procedure for determining the quality and quantity of oocytes in the controlled ovarian stimulation (COS) process of the in-vitro fertilization (IVF) treatment cycle. The current approach for determining the GSD is mainly based on the experience of a clinician, lacking unified and scientific standards. This study aims to establish a prediction model of GSD, based on which good COS outcomes can be achieved with the influencing factors comprehensively evaluated quantitatively. MATERIAL AND
methodsWe collected a total of 1555 patients undergoing the first oocytes retrieving cycle and conducted correlation analysis to find the significant factors related to the GSD. Two GSD models are built based on two popular machine learning approaches, and the one with better model performance is selected as the final model. Finally, clinical application and validation were conducted to verify the effectiveness of the proposed model.
results(1) Age, duration of infertility, type of infertility, body mass index (BMI), antral follicle count (AFC), basal follicle stimulating hormone (bFSH), estradiol (E
conclusionModeling the GSD value with machine learning approaches is feasible and effective, and the proposed model has good clinical application for determining the GSD in the IVF treatment cycle.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.