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Background: Robotics in arthroplasty remains controversial due to the uncertainty of clinical outcomes in robotic total knee arthroplasty (rTKA). This study aimed to compare the time to achieve the minimal clinically important difference (MCID) between rTKA and manual TKA (mTKA).
Methods: A total of 726 TKAs (416 robotic and 310 manual) were analyzed. We conducted a retrospective analysis of 726 TKAs performed between 2019 and 2022. Patient-reported outcomes were assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) global physical, PROMIS physical function-10a (PF-10a), and Knee Injury and Osteoarthritis Outcome Score-physical function short-form (KOOS-PS) scores, both preoperatively and postoperatively. Survival curves, accounting for interval censoring, were utilized to evaluate the time to achieve MCID. Statistical comparisons between groups were made using log-rank and weighted log-rank tests.
Results: Comparing time to achieve MCID without interval censoring, the median time for rTKA was significantly lower than mTKA for PROMIS global physical (3.5 versus 3.7 months, P = 0.032) and KOOS-PS (3.7 versus 5.3 months, P = 0.002) but similar for PROMIS PF-10a (6.0 versus 6.7 months, P = 0.16). Notably, interval censoring showed similar times to achieve MCID for rTKA and mTKA in PROMIS global physical (0.53 to 0.54 versus 1.23 to 1.24 months, P = 0.31), PROMIS PF-10a (3.03 to 3.03 versus 2.17 to 2.17 months, P = 0.89), and KOOS-PS (1.47 to 1.47 versus 2.17 to 2.17 months, P = 0.27).
Concusions: Using time to MCID methodology, the median time to achieve MCID did not differ by surgical technique. The present study offers valuable patient-centric insights into preoperative expectations management and patient education. Further prospective studies with more granular patient-reported outcomes measurement collection are needed to evaluate the true effectiveness of robotics in arthroplasty.
Level Of Evidence: Level III, retrospective comparative study.
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http://dx.doi.org/10.1016/j.arth.2025.02.031 | DOI Listing |
Comput Methods Biomech Biomed Engin
September 2025
Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India.
Parkinson's disease (PD) is a neurodegenerative condition that impairs motor functions. Accurate and early diagnosis is essential for enhancing well-being and ensuring effective treatment. This study proposes a deep learning-based approach for PD detection using EEG signals.
View Article and Find Full Text PDFFront Rehabil Sci
August 2025
Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
Introduction: Spinal cord injury (SCI) presents a significant burden to patients, families, and the healthcare system. The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.
Methods: We conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes.
Background: Transforming Clinical Practice Guideline (CPG) recommendations into computer readable language is a complex and ongoing process that requires significant resources, including time, expertise, and funds. The objective is to provide an extension of the widely used GIN-McMaster Guideline Development Checklist (GDC) and Tool for the development of computable guidelines (CGs).
Methods: Based on an outcome from the Human Centered Design (HCD) workshop hosted by the Guidelines International Network North America (GIN-NA), a team was formed to develop the checklist extension.
iScience
September 2025
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.
Monopulse radar angle measurement technology is crucial for modern missile precision guidance systems due to its high accuracy and real-time capabilities. Cross-eye jamming (CEJ) is recognized as one of the most effective countermeasures against monopulse radar. However, traditional CEJ implementation requires complex amplitude and phase modulation through specialized hardware.
View Article and Find Full Text PDFMater Today Bio
October 2025
Yunnan Key Laboratory of Breast Cancer Precision Medicine, Institute of Biomedical Engineering, Kunming Medical University, Kunming, 650500, Yunnan, China.
Achieving precise intratumoral accumulation and coordinated activation remains a major challenge in nanomedicine. Photothermal therapy (PTT) provides spatiotemporal control, yet its efficacy is hindered by heterogeneous distribution of PTT agents and limited synergy with other modalities. Here, we develop a dual-activation nanoplatform (IrO-P) that integrates exogenous photothermal stimulation with endogenous tumor microenvironment (TME)-responsive catalysis for synergistic chemodynamic therapy (CDT) and ferroptosis induction.
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