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Abstract

Supporting patients in making informed healthcare decisions is a cornerstone of ethical medical practice. Surgeons frequently draw for and show images to patients when consenting them for operations but the value of this practice in informed decision-making is unclear. An audit was conducted in a General Surgery Department. 244 patients completed questionnaires on the value of visual materials when giving consent for surgery. The complexity of the operations was classified into “simple”, “moderate” or “complex”. 100% of patients felt they had given informed consent to surgery. 62% of patients received at least one form of visual material during the consenting process. All patients who received a drawing, and 99% of those provided with other images, valued these resources. Visual materials were considered more useful to patients when giving consent for moderate or complex operations than simple ones. Approximately one third of patients who did not receive visual materials would have appreciated these when making an informed decision. This research highlights the value of surgeons drawing for, and providing other visual resources to, their patients as part of the consent process. There is a role for further research and training materials in drawing skills for surgeons.  相似文献   
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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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