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Nicholas E. Anton Tomoko Mizota Lava R. Timsina Jake A. Whiteside Erinn M. Myers Dimitrios Stefanidis 《American journal of surgery》2019,217(2):266-271
Introduction
Several studies demonstrated that simulator-acquired skill transfer to the operating room is incomplete. Our objective was to identify trainee characteristics that predict the transfer of simulator-acquired skill to the operating room.Methods
Trainees completed baseline assessments including intracorporeal suturing (IS) performance, attentional selectivity, self-reported use of mental skills, and self-reported prior clinical and simulated laparoscopic experience and confidence. Residents then followed proficiency-based laparoscopic skills training, and their skill transfer was assessed on a live-anesthetized porcine model. Predictive characteristics for transfer test performance were assessed using multiple linear regression.Results
Thirty-eight residents completed the study. Automaticity, attentional selectivity, resident perceived ability with laparoscopy and simulators, and post-training IS performance were predictive of IS performance during the transfer test.Conclusions
Promoting automaticity, self-efficacy, and attention selectivity may help improve the transfer of simulator-acquired skill. Mental skills training and training to automaticity may therefore be valuable interventions to achieve this goal. 相似文献4.
Danielle E Whittier Elizabeth J Samelson Marian T Hannan Lauren A Burt David A Hanley Emmanuel Biver Pawel Szulc Elisabeth Sornay-Rendu Blandine Merle Roland Chapurlat Eric Lespessailles Andy Kin On Wong David Goltzman Sundeep Khosla Serge Ferrari Mary L Bouxsein Douglas P Kiel Steven K Boyd 《Journal of bone and mineral research》2022,37(3):428-439
Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR). 相似文献
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Sebastian P. Mondaca MD Dazhi Liu PharmD BCOP Jessica R. Flynn Sandy Badson Stefan Hamaway BS Mrinal M. Gounder MD Danny N. Khalil MD PhD Alexander E. Drilon MD Bob T. Li MD MPH Komal L. Jhaveri MD Alison M. Schram MD Katherine E. Kargus RN Mary Kate Kasler DNP MSN Natalie M. Blauvelt Neil H. Segal MD PhD Marinela Capanu PhD Margaret K. Callahan MD PhD David M. Hyman MD Maya Gambarin-Gelwan MD James J. Harding MD 《Cancer》2020,126(22):4967-4974
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