Optimal patient care is best defined in terms of outcome. Reliable laboratory test results are important to patient safety. The laboratory must use the tools available to it to minimize the uncertainty of measurement. Three sources of error contribute to uncertainty: Intermethod bias, which is minimized and trueness of measure maximized when laboratories use calibrators and methods traceable to higher order, reference standards; imprecision inherent in the analysis, which is seen as small differences between replicate tests; interference from factors external to the test itself, which are seen as erroneous values markedly deviant from trueness. Although improbable, such contributions to total analytical error may be the most misleading. Risk is best managed by identifying the sources of error and controlling for those sources most likely to contribute to total analytical error. Comprehensive control of error requires the laboratory scientist and physicians caring for patients to work together to ensure interpretability of results. Practice guidelines are available from the Clinical and Laboratory Standards Institute to address each of these factors.
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ESMO Open
January 2025
Dana-Farber Cancer Institute, Boston, USA; Harvard School of Public Health, Boston, USA.
Background: Cancer researchers frequently consider the use of single-arm and randomized controlled clinical trial designs that leverage external data. The literature has reported extensively on how the use of external data can introduce bias through a variety of distortion mechanisms. In this article, we focus on a distortion mechanism that is often overlooked: informative censoring.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducted to analyze the performance of nine ensembles and regular machine learning (ML) methods in predicting two water quality parameters including total dissolved solids (TDS) and pH, in an area with semi-arid climate conditions.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Illinois Institute of Technology, Chicago, IL, USA.
Background: Elevated iron in brain is a source of free radicals that causes oxidative stress which has been linked to neuropathologies and cognitive impairment among older adults. The aim of this study was to investigate the association of iron levels with transverse relaxation rate, R, and white matter hyperintensities (WMH), independent of the effects of other metals and age-related neuropathologies.
Method: Cerebral hemispheres from 437 older adults participating in the Rush Memory and Aging Project study (Table 1) were imaged ex-vivo using 3T MRI scanners.
Sci Rep
January 2025
Photonics Laboratory, Tampere University, 33104, Tampere, Finland.
Supercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning and metaheuristic algorithms offer more efficient optimization approaches. We report here an experimental study of supercontinuum spectral shaping by tuning the phase of the input pulses, different optimization approaches including a genetic algorithm, particle swarm optimizer, and simulated annealing.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A3E9, Canada.
One of the green, clean, and environment-friendly sources of energy is wind energy. For the assessment of wind energy potential, the parameters of the probability distribution function (PDF), i.e.
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