BACKGROUND: The Letournel and Judet classification system is commonly used for classifying acetabular fractures. However, for orthopaedic surgeons with less experience with these fractures, correct classification can be more difficult. A stepwise approach has been suggested to enhance the inexperienced observer's ability to properly classify acetabular fractures, but it is unclear whether this actually improves one's ability. QUESTIONS/PURPOSES: We asked (1) whether the use of a step-by-step algorithm improves residents' ability to classify acetabular fractures, (2) whether resident experience influenced ability to correctly classify acetabular fractures, and (3) which acetabular fractures were the most difficult to classify? METHODS: Forty-six residents reviewed 15 sets of plain radiographs of 10 acetabular fracture patterns. Residents used the Letournel and Judet classification with only a diagram for reference. Three weeks later they were asked to classify the fractures a second time with the use of the algorithm. We then compared the number of correct responses from the two sessions and determined whether resident experience and use of the algorithm influenced correct classification. RESULTS: We found an improvement in the number of correctly classified fractures between the first (348/690 [50%]) and second (409/690 [59%]) sessions. Thirty-two of 46 participants improved their score with the use of the algorithm. There was a tendency for participants with more residency training to correctly classify the fractures. CONCLUSIONS: The algorithm provided modest improvement to the residents' ability to classify acetabular fractures. This or other such algorithms could provide residents with a basic tool to better evaluate standard radiographs and classify acetabular fractures.
BACKGROUND: The Letournel and Judet classification system is commonly used for classifying acetabular fractures. However, for orthopaedic surgeons with less experience with these fractures, correct classification can be more difficult. A stepwise approach has been suggested to enhance the inexperienced observer's ability to properly classify acetabular fractures, but it is unclear whether this actually improves one's ability. QUESTIONS/PURPOSES: We asked (1) whether the use of a step-by-step algorithm improves residents' ability to classify acetabular fractures, (2) whether resident experience influenced ability to correctly classify acetabular fractures, and (3) which acetabular fractures were the most difficult to classify? METHODS: Forty-six residents reviewed 15 sets of plain radiographs of 10 acetabular fracture patterns. Residents used the Letournel and Judet classification with only a diagram for reference. Three weeks later they were asked to classify the fractures a second time with the use of the algorithm. We then compared the number of correct responses from the two sessions and determined whether resident experience and use of the algorithm influenced correct classification. RESULTS: We found an improvement in the number of correctly classified fractures between the first (348/690 [50%]) and second (409/690 [59%]) sessions. Thirty-two of 46 participants improved their score with the use of the algorithm. There was a tendency for participants with more residency training to correctly classify the fractures. CONCLUSIONS: The algorithm provided modest improvement to the residents' ability to classify acetabular fractures. This or other such algorithms could provide residents with a basic tool to better evaluate standard radiographs and classify acetabular fractures.
Authors: Brad A Petrisor; Mohit Bhandari; R Douglas Orr; Scott Mandel; Desmond C Kwok; Emil H Schemitsch Journal: Arch Orthop Trauma Surg Date: 2003-04-26 Impact factor: 3.067
Authors: Bennet A Butler; Ryan S Selley; Colin K Cantrell; Richard W Nicolay; Cort D Lawton; Sohaib Z Hashmi; Kevin R Carlile; Michael D Stover Journal: Cureus Date: 2022-01-21