1,721,054 research outputs found
An insight of how multiple skeletal maturity indices can be used for growth assessment: relationship between the simplified olecranon, simplified digital, and distal radius and ulna classifications
This cross-sectional study aims to investigate the relationship between the simplified olecranon, simplified digital, and distal radius and ulna (DRU) classifications, and whether they can aid in more comprehensive maturity assessment together. Left hand and wrist and lateral elbow radiographs from pediatric patients were assessed using the three skeletal maturity indices. The association between maturity indices was investigated using Goodman and Kruskal’s gamma, and by mapping of individual grades based on chronological age. Specific maturity grades, at which peak height velocity (PHV) occurs as previously identified, were based upon to explore how the three systems interact. A total of 114 patients (63.2% girls) were studied. Correlations and associations between the three maturity parameters were significant (all at P<0.001). Mapping revealed uneven spans and coverage of different periods by each index. Olecranon stage 1 coincided with R3 (for girls), R4 (for boys), U3, and SS1. Olecranon stage 5 occurred as early as R7, U6, and SS4. Upon elbow fusion, the simplified digital (SS5–SS8) and DRU (R8–R11 and U7–U9) classifications can be used for assessment until maturity. The inter-relationship of the simplified hand, wrist, and olecranon methods indicates their combined use. DRU grades can be used in growth periods which are less well covered. Prepubertal and growth acceleration phase of pubertal growth spurt can best be assessed by both the simplified olecranon (stages 1–3) and DRU classifications (R1–R5 and U1–U4). All three indices are required during PHV. For post-PHV, DRU (R8–R11 and U7–U9) and simplified digital method (SS5–SS8) complement each other for assessment until skeletal maturity
Long-Term Progression Risk In Skeletally Mature Patients with Adolescent Idiopathic Scoliosis
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Investigating Disease Burden, Management Approaches, and Clinical Course Prediction of Low Back Pain Using Machine Learning Based on Data From 6,426 Patients in Hong Kong.
Learning Powered Axial Vertebral Rotation Analysis for Adolescent Idiopathic Scoliosis: Technology Development and Clinical Validation
Radiation-free 3D Spine Curve Synthesis Based on Biplanar Optical Unclothed Back Images for Spine Alignment Analysis
Poster Presentation: A Randomized Controlled Trial Comparing Gradual and Immediate Brace Weaning for Clinical Management of Adolescent Idiopathic Scoliosis Patients
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