Rationale and Objectives
The conceptualization of acetabular fractures can present a daunting challenge to radiology residents. 3D models have been shown to aid in the spatial perception of complicated anatomy and may help residents grasp the elaborate classification systems for these anatomically complex fractures. Prior studies have explored the utility of 3D printed models for surgical planning in various settings. To our knowledge, no study has evaluated their efficacy in radiology resident training.
Materials and Methods
Following IRB approval, 22 radiology residents were randomized and stratified by Post Graduate Year into two groups of 11 residents. Both groups received separate identical presentations on the 5 most common acetabular fractures given by a musculoskeletal trained radiologist. Residents in the experimental group received 3D printed models of the five most common fracture types with which to interact during the presentation, while the control group did not. Both groups received a pretest and a follow up posttest three weeks later.
Results
A Wilcoxon rank sum test was performed to determine if statistically significant differences between the pretest and posttest scores of the experimental and control groups existed. There was no statistically significant difference in scores on the pre-test, which confirmed successful randomization. There was a statistically significant difference (P = 0.02) on the posttest scores between the experimental and control groups.
Conclusion
3D printed models promise as an effective educational tool for resident learning with respect to acetabular fractures, improving short-term understanding of complex anatomy and classification systems.
INTRODUCTION
Three Dimensional (3D) printing has increasingly become prevalent throughout medicine, particularly in its applications within neurosurgery, orthopedics, pulmonology, and cardiology ( ). The use of these models to enhance surgical simulation, pre-operative planning, intra-operative guidance, and for creating implants/prostheses has been well studied and documented ( ). However, much less has been studied on the educational effects of 3D models on education, learning, and retention of information. Preliminary studies have suggested that these models enhance knowledge of complex anatomy and understanding of difficult concepts for medical trainees ( ).
Understanding the classification of acetabular fractures is one among many applications of 3D printed models, given the complexity of the anatomy. This is particularly relevant in orthopedics and radiology, as an accurate diagnosis of the type of acetabular fracture influences treatment and outcomes; since different types of acetabular fractures are repaired by different surgical approaches and techniques ( ). The most widely used classification for acetabular fractures is the Judet and Letournel classification, which classifies fractures in 10 different types; with five (both column type, T-shaped, transverse, transverse with posterior wall, and isolated posterior wall) of the ten types accounting for approximately 90% of acetabular fractures seen on imaging ( ).
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MATERIALS AND METHODS
Study Population
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Table 1
Baseline Characteristics of Study Sample
Intervention Group Control Group Mean age (years) 30.0 30.1 Sex Male 7 (63.6%) 8 (72.7%) Female 4 (36.4%) 3 (27.3%) Number of residents 11 11 PGY*2 3 3 PGY3 3 3 PGY4 2 3 PGY5 3 2
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3D Printed Models
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Study Design
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Outcome Ascertainment and Statistical Analyses
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RESULTS
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Table 2
Distribution of Mean and Median Test Scores by Study sample, Intervention Group, and Control Group
Entire study Intervention Group Control Group sample (N=11 residents) (N=22 residents) (N=11 residents) Mean score on pretest (standard deviation) 37.7 (13.4) 36.4 (15.0) 39.1 (12.2) Median score on pretest 40 40 40 Mean score on posttest (standard deviation) 63.6 (16.8) 71.8 (18.3) 55.5 (10.4) Median score on posttest 60 70 50 Mean difference in scores (standard deviation) 25.9 (21.5) 35.5 (23.4) 16.4 (15.0) Median difference in scores 25 30 20
Table 3
Distribution of Mean and Median Test Scores by Post Graduate Level (PGY) of Training.
(N = 6 residents) (N = 6 residents) (N = 5 residents) (N = 5 residents) Mean score on Pretest (Standard deviation) 38.3 (7.53) 41.7 (14.7) 38 (13.0) 32 (19.2) Median score on Pretest 40 45 40 30 Mean score on Posttest (Standard deviation) 58.3 (14.7) 71.7 (19.4) 58.0 (8.4) 66.0 (21.9) Median score on Posttest 55 75 60 60 Mean difference in scores (Standard deviation) 20.0 (14.1) 30.0 (32.2) 20.0 (12.2) 34.0 (23.0) Median difference in scores 20 25 20 30
Table 4
Results of the Wilcoxon Rank Sum Tests by Pretest, Posttest, and Difference between Pretest and Post test.
Pretest Posttest Difference between Pretest and Posttest P Value 0.74 0.02 0.04
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DISCUSSION
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CONCLUSION
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