FindTrial
Sign In

Construction of a Machine Learning-Based Prediction Model for Postpartum Pelvic Organ Prolapse

Construction of a Machine Learning-Based Prediction Model for Postpartum Pelvic Organ Prolapse

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600124489
Enrollment
Unknown
Registered
2026-05-13
Start date
2025-11-30
Completion date
Unknown
Last updated
2026-05-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pelvic organ prolapse, POP

Interventions

Control group:None
POP group:None

Sponsors

The First Affiliated Hospital of Army Medical University
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
18 Years to 45 Years

Inclusion criteria

Inclusion criteria: 1.Postpartum period >=42 days and <=92 days; 2.Age 18-45 years; 3.No prior pelvic floor treatment;

Exclusion criteria

Exclusion criteria: 1.Preterm delivery (<37 weeks gestation) or multiple gestation (e.g., twins or higher-order pregnancies); 2.Comorbid severe cardiac, hepatic, or renal diseases, acute pelvic infections, history of orthopedic surgery, or psychiatric disorders;

Design outcomes

Primary

MeasureTime frame
Pelvic Organ Prolapse Quantification (POP-Q) Stage;—

Countries

China

Contacts

Public ContactYang Hong

The First Affiliated Hospital of Army Medical University

619379661@qq.com+86 23 68766398

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: May 22, 2026