Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Nov 25;16(1):230.
doi: 10.1186/s13048-023-01310-2.

Progress of the application clinical prediction model in polycystic ovary syndrome

Affiliations

Progress of the application clinical prediction model in polycystic ovary syndrome

Guan Guixue et al. J Ovarian Res. .

Abstract

Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.

Keywords: Application progress; Clinical prediction model; Endocrine; Overweight PCOS; Polycystic ovary syndrome; Reproduction.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A summary of progress of the application clinical prediction model in polycystic ovary syndrome

Similar articles

Cited by

References

    1. Adams ST, Leveson SH. Clinical prediction rules. BMJ. 2012;344:d8312. doi: 10.1136/bmj.d8312. - DOI - PubMed
    1. Ranstam J, Cook JA, Collins GS. Clinical prediction models. Br J Surg. 2016;103(13):1886. doi: 10.1002/bjs.10242. - DOI - PubMed
    1. Hemingway H, Croft P, Perel P, et al. Prognosis research strategy [PROGRESS] 1: a framework for researching clinical outcomes. BMJ. 2013;346:e5595. doi: 10.1136/bmj.e5595. - DOI - PMC - PubMed
    1. Riley RD, Hayden JA, Steyerberg EW, et al. Prognosis research strategy [PROGRESS] 2: prognostic factor research. PLoS Med. 2013;10(2):e1001380. doi: 10.1371/journal.pmed.1001380. - DOI - PMC - PubMed
    1. Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis research strategy [PROGRESS] 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. doi: 10.1371/journal.pmed.1001381. - DOI - PMC - PubMed

Publication types