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The femur is the body's longest and strongest bone spanning the thigh region. Its head articulates with the acetabulum of the hip bone to form the hip joint. A minor indentation on the medial side of the femoral head, called the fovea capitis, serves as the site of attachment for the ligament of the head of the femur. This weak ligament spans the femur and acetabulum and supports the hip joint. The narrowed region below the head is the neck of the femur. The inclination angle between the...
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No significant differences in nonunion rates between static and dynamic intramedullary nail fixation for non-comminuted femoral shaft fractures.

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Updated: May 5, 2026

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'À la carte' treatment algorithm for patellofemoral instability.

T Pineda1, D H Dejour2

  • 1Hospital El Carmen, Santiago, Chile; Facultad de Medicina, Universidad Andrés Bello, Santiago, Chile; Hospital del Trabajador, Santiago, Chile.

Revista Espanola De Cirugia Ortopedica Y Traumatologia
|February 5, 2025
PubMed
Summary

Patellar instability is a complex knee problem. A treatment algorithm helps surgeons identify risks and plan corrective surgery for better outcomes.

Keywords:
Displasia troclearInestabilidad patelofemoralLigamento patelofemoral medialLuxación de rótulaMedial patellofemoral ligamentPatellar dislocationPatellofemoral instabilityTrochlear dysplasia

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Area of Science:

  • Orthopedics
  • Knee Surgery
  • Biomechanical Engineering

Background:

  • Patellar instability presents complex challenges in treatment.
  • Identifying pre-operative risk factors is crucial for successful surgical outcomes.
  • Current decision-making processes can be difficult for surgeons.

Purpose of the Study:

  • To present a treatment algorithm from the Lyon School of Knee Surgery.
  • To guide surgeons in identifying imaging risk factors for treatment failure.
  • To propose a therapeutic plan focused on correcting major risk factors.

Main Methods:

  • Development of a treatment algorithm based on the Lyon School of Knee Surgery principles.
  • Utilizing imaging to identify specific risk factors associated with patellar instability.
  • Tailoring therapeutic strategies to address identified major risk factors.

Main Results:

  • The algorithm provides a structured approach to patellar instability management.
  • Identifies key imaging markers predictive of surgical treatment failure.
  • Facilitates personalized treatment plans by addressing specific patient risk factors.

Conclusions:

  • The Lyon School of Knee Surgery algorithm aids in optimizing treatment decisions for patellar instability.
  • Accurate identification of imaging risk factors improves surgical planning.
  • A tailored approach focusing on correcting major risks enhances patient outcomes.