Background: With this study, we sought to quantify perioperative changes in driving performance among patients who underwent anatomic or reverse shoulder arthroplasty.
Methods: Using a driving simulator, 30 patients (20 anatomic and 10 reverse total shoulder arthroplasties) were tested preoperatively and at 2 weeks (PO2), 6 weeks (PO6), and 12 weeks (PO12) postoperatively. The total number of collisions, centerline crossings, and off-road excursions (when the vehicle traversed the lateral road edge), and scores on a visual analog scale (VAS) for pain and the Shoulder Pain and Disability Index (SPADI) were recorded at each driving trial.
Results: The mean number of collisions increased from 5.9 preoperatively to 7.4 at PO2 and subsequently decreased to 5.6 at PO6 and 4.0 at PO12 (p = 0.0149). In addition, the number of centerline crossings decreased from 21.4 preoperatively to 16.3 at PO12 (p < 0.05). Multivariate analysis of the data demonstrated that increased VAS for pain scores, older age, and less driving experience had a negative impact on driving performance.
Conclusions: Driving performance returned to preoperative levels at 6 weeks after shoulder arthroplasty. By 12 weeks postoperatively, patients demonstrated improved driving performance compared with preoperative performance. On the basis of our findings, clinicians can suggest a window of 6 to 12 weeks postoperatively for the gradual return to driving. However, for patients of older age, with less driving experience, or with greater pain, a return to driving at closer to 12 weeks postoperatively should be recommended.
Level Of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
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http://dx.doi.org/10.2106/JBJS.15.00162 | DOI Listing |
J Exp Clin Cancer Res
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School of Medicine, Chinese PLA General Hospital, Nankai University, Beijing, China.
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Hebei Agricultural University, Baoding, China.
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January 2025
Jilin Key Laboratory of Solid-State Laser Technology and Application, School of Science, Changchun, 130022, Jilin, China.
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January 2025
College of Intelligent systems Science and Engineering, Harbin Engineering University, Harbin, 150006, China.
Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem.
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