Development and validation of diagnostic prediction models for central precocious puberty in girls based on machine learning: a multicenter retrospective study - Scorecard - MDSpire

Development and validation of diagnostic prediction models for central precocious puberty in girls based on machine learning: a multicenter retrospective study

  • By

  • Wenyong Wu

  • Zhe Su

  • Haiyan Wei

  • Yanhong Li

  • Benlong Zhu

  • Xin Yuan

  • Daibin Lei

  • Yi Wei

  • Xian Wu

  • Hanghan Ou

  • Xinyu Chen

  • Ziling Zhu

  • Ruimin Chen

  • June 15, 2026

  • 0 min

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Clinical Scorecard: Creation and assessment of machine learning-based diagnostic prediction models for central precocious puberty in females: a multicenter retrospective analysis

At a Glance

CategoryDetail
Condition
Key Mechanisms
Target PopulationGirls diagnosed with Precocious Puberty (PP) before age 7.5 years.
Care Setting

Key Highlights

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Guideline-Based Recommendations

Diagnosis

    Management

    • Utilization of machine learning models to assist in diagnosing CPP, as suggested by the study.

    Monitoring & Follow-up

      Risks

        Patient & Prescribing Data

        Inclusion of clinical indicators such as disease course and hormonal levels for diagnosis, as per the study.

        Clinical Best Practices

        • remove

        Related Resources & Content

        Original Source(s)

        Development and validation of diagnostic prediction models for central precocious puberty in girls based on machine learning: a multicenter retrospective study

        • by Wenyong Wu, Zhe Su, Haiyan Wei, Yanhong Li, Benlong Zhu, Xin Yuan, Daibin Lei, Yi Wei, Xian Wu, Hanghan Ou, Xinyu Chen, Ziling Zhu, Ruimin Chen

        • June 15, 2026

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