Purpose: Distal radius fractures are a common injury. In the emergency room, trainees regularly assess these fractures using visual estimation. Our hypothesis is that assessment of radiographic parameters has sufficient accuracy for rendering treatment consistent with formal measurements.
Methods: This study compared visual measurements made by 25 orthopaedic residents and attending physicians to formal measurements made by a single fellowship trained musculoskeletal radiologist in a series of patients with distal radius fractures. A search was performed utilizing the ICD-9 code for distal radius fracture in all patients presenting to a single institution emergency department. Participants used visual estimation to rate 25 radiographs. Parameters estimated included radial inclination, radial height, volar tilt, and the presence of intra-articular displacement. Analysis using Lin concordance coefficients, Bland Altman plots, and the Kappa statistic evaluated the agreement between visual estimation and formal measurements. The proportion of raters whose estimates would have resulted in a course of treatment that conflicted with the formal reading quantified the potential impact of visual estimation on treatment.
Results: Concordance coefficients were poor for radial inclination (ρc = 0.13), radial height (ρc = 0.24), and volar tilt (ρc = 0.46). The Kappa statistic for intra-articular displacement was 0.4. Analysis performed according to level of training did not result in substantial improvements in these statistics. Treatment based on visual estimates conflicted with formal readings 34 % of the time for radial inclination, 38 % of the time for radial height, 27 % of the time for volar tilt, and 31 % of the time for intra-articular displacement.
Discussion: Visual estimation is not an adequate form of measurement for evaluation of patients with distal radius fractures. Physicians should be mindful of these results when developing treatment plans based solely upon visual estimation.
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http://dx.doi.org/10.1007/s11552-014-9666-2 | DOI Listing |
Comput Biol Med
December 2024
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan. Electronic address:
Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard methods, especially in healthcare settings. Among the biggest threats to global public health is the fast spread of malaria.
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Ege University Medical School, Department of Neurology, 35100, İzmir, Turkey.
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Lasers Med Sci
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Department of Prosthodontics, Faculty of Dentistry, Sabzevar University of Medical Sciences, Sabzevar, Iran.
Purpose: This systematic review and meta-analysis aimed to assess the gingival crevicular fluid (GCF) level of tumor necrosis factor-alpha (TNF-alpha) as a valuable inflammatory cytokine for estimation of the efficacy of adjunctive antimicrobial photodynamic therapy (aPDT) in stage II-IV periodontitis patients.
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BMC Med Inform Decis Mak
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Hellenic Complex Systems Laboratory, Kostis Palamas 21, 66131, Drama, Greece.
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Objective: The aim of this work is to introduce a software tool developed in the Wolfram Language for the parametric estimation, visualization, and comparison of Bayesian diagnostic measures and their uncertainty.
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BMC Med Res Methodol
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German Diabetes Center, Leibniz Center for Diabetes Research, Institute for Biometrics and Epidemiology, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
Background: Propensity score matching has become a popular method for estimating causal treatment effects in non-randomized studies. However, for time-to-event outcomes, the estimation of hazard ratios based on propensity scores can be challenging if omitted or unobserved covariates are present. Not accounting for such covariates could lead to treatment estimates, differing from the estimate of interest.
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