ArticleWorld journal of orthopedics2024
Total hip arthroplasty preoperative planning for childhood hip disorders' sequelae: Focus on developmental dysplasia of the hip.
Article in World journal of orthopedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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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Who cites it
4 citing papers in PubMed.
- Total hip replacement in adolescents.Orthopadie (Heidelberg, Germany) · 2026Review
- Learning Curves Associated With Robotic Total Hip Arthroplasty: A Scoping Review.Orthopaedic surgery · 2025Article
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Artificial Intelligence Approach in Hip Prosthesis Identification and Addressing Radiographic Outcome Measures.Arthroplasty today · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developmental dysplasia of the hip (DDH) poses significant challenges in both childhood and adulthood, affecting up to 10 per 1000 live births in the United Kingdom and United States. While newborn screening aims to detect DDH early, missed diagnoses can lead to severe complications such as hip dysplasia and early onset osteoarthritis in adults. Treatment options range from less invasive procedures like hip-preserving surgery to more extensive interventions such as total hip arthroplasty (THA), depending on the severity of the condition. Preoperative planning plays a critical role in optimizing surgical outcomes for DDH patients undergoing THA. This includes accurate imaging modalities, precise measurement of acetabular bone stock, assessment of femoral head subluxation, and predicting prosthesis size and leg length discrepancy. Recent advancements artificial intelligence and machine learning offer promising tools to enhance preoperative planning accuracy. However, challenges remain in validating these technologies and integrating them into clinical practice. This editorial highlights the importance of ongoing research to refine preoperative strategies and improve outcomes in DDH management through evidence-based approaches and technological innovations.
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