From Orlando Health Jewett Orthopedic Institute, Orlando, FL (Shaath and Avilucea), the Department of Orthopaedic Surgery, University of California Irvine, Irvine, CA (Lim), and the Division of Orthopaedic Trauma, Department of Orthopaedic Surgery, McGovern Medical School at UTHealth, Houston, TX ("Chip" Routt).
J Am Acad Orthop Surg. 2022 Jan 15;30(2):79-83. doi: 10.5435/JAAOS-D-20-00872.
CT and three-dimensional (3D) CT reconstructions have been shown to improve the understanding of acetabular fractures. With the increased availability of 3D pelvic CT reconstructions, our goal for this study was to develop an algorithm to aid residents in the classification of acetabular fractures. We hypothesized that the use of a stepwise algorithm will markedly enhance the trainees' ability to correctly identify acetabular fracture patterns.
This was a multicenter study that included 33 residents. Residents reviewed 15 sets of 3D reconstructions of the 10 acetabular fracture patterns. Residents completed the first round, and the results were collected electronically. Three weeks later, they were asked to classify the fractures a second time with the use of the algorithm. The number of correct responses from the two sessions was analyzed to determine if the algorithm improved residents' ability to correctly classify fracture patterns.
Thirty-three residents classified 15 fractures which yielded 495 unique responses. Residents correctly classified 52.5% (260/495) of fractures without the algorithm, which significantly increased to 77.5% (384/495) (P = 0.001) with the algorithm. When stratified by year in residency, all residents were able to correctly classify markedly more fractures with the algorithm.
Overall, we believe this method is a reproducible diagnostic tool that will assist residents in classifying acetabular fractures. We were able to demonstrate that with the use of this algorithm, residents' ability to correctly classify acetabular fractures is markedly enhanced, regardless of year in training. This algorithm will be a useful adjunct to assist and advance trainees' education and understanding of a complex topic.
CT 和三维（3D）CT 重建已被证明可提高对髋臼骨折的理解。随着 3D 骨盆 CT 重建的可用性增加，我们这项研究的目的是开发一种算法来帮助住院医师对髋臼骨折进行分类。我们假设使用逐步算法将显著提高住院医师正确识别髋臼骨折模式的能力。
这是一项多中心研究，包括 33 名住院医师。住院医师回顾了 10 种髋臼骨折模式的 15 组 3D 重建。住院医师完成了第一轮，结果以电子方式收集。3 周后，他们被要求使用算法第二次对骨折进行分类。分析两次会议的正确回答数，以确定算法是否提高了住院医师正确分类骨折模式的能力。
33 名住院医师分类了 15 处骨折，共产生了 495 个独特的反应。住院医师在没有算法的情况下正确分类了 52.5%（260/495）的骨折，而使用算法后显著增加到 77.5%（384/495）（P = 0.001）。按住院年限分层，所有住院医师都能够使用算法正确分类更多的骨折。
总体而言，我们认为这种方法是一种可重复使用的诊断工具，将有助于住院医师对髋臼骨折进行分类。我们能够证明，使用该算法，住院医师正确分类髋臼骨折的能力显著提高，无论培训年限如何。该算法将是一种有用的辅助手段，可帮助和促进住院医师对复杂课题的教育和理解。
