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When poor reliability of "output" variables is reported, it can be difficult to discern whether blame lies with the measurement (ie, the inputs) or the overarching concept. This commentary addresses this issue, using the force-velocity-power (FvP) profile in jumping to illustrate the interplay between concept, method, and measurement reliability. While FvP testing has risen in popularity and accessibility, some studies have challenged the reliability and subsequent utility of the concept itself without clearly considering the potential for imprecise procedures to impact reliability measures. To this end, simulations based on virtual athletes confirmed that push-off distance and jump-height variability should be <4% to 5% to guarantee well-fitted force-velocity relationships and acceptable typical error (<10%) in FvP outputs, which was in line with previous experimental findings. Thus, while arguably acceptable in isolation, the 5% to 10% variability in push-off distance or jump height reported in the critiquing studies suggests that their methods were not reliable enough (lack of familiarization, inaccurate procedures, or submaximal efforts) to infer underpinning force-production capacities. Instead of challenging only the concept of FvP relationship testing, an alternative conclusion should have considered the context in which the results were observed: If procedures' and/or tasks' execution is too variable, FvP outputs will be unreliable. As for some other neuromuscular or physiological testing, the FvP relationship, which magnifies measurement errors, is unreliable when the input measurements or testing procedures are inaccurate independently from the method or concept used. Field "simple" methods require the same methodological rigor as "lab" methods to obtain reliable output data.
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http://dx.doi.org/10.1123/ijspp.2021-0535 | DOI Listing |
Adv Health Sci Educ Theory Pract
September 2025
Division of Human Sciences, NOSM University, Thunder Bay, ON, Canada.
Innovative qualitative approaches are essential for exploring how health professions education (HPE) can address complex, value-laden constructs such as social accountability. Visual elicitation techniques, including rich picture interviews (RPIs), offer distinctive opportunities to surface layered, affective, and contextually embedded understandings. This methodological study examines participant perspectives on the use of RPIs within a broader qualitative interpretive description on social accountability.
View Article and Find Full Text PDFDrugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDFClin Res Cardiol
September 2025
Department of Cardiology, University Heart Center, University Hospital Zurich, Center for Translational and Experimental Cardiology (CTEC), University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
Background: Diabetic patients with ST-segment elevation myocardial infarction (STEMI) are at an increased risk of cardiovascular events as compared to non-diabetic patients. This analysis investigated outcomes of diabetic patients presenting with multivessel disease (MVD) and STEMI in a contemporary trial and the relevance of an immediate versus staged multivessel PCI strategy in this high-risk population.
Methods: Patients enrolled in the MULTISTARS AMI trial were stratified according to the presence/absence of diabetes.
J Chem Theory Comput
September 2025
Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria.
We present a novel, flexible framework for electronic structure interfaces designed for nonadiabatic dynamics simulations, implemented in Python 3 using concepts of object-oriented programming. This framework streamlines the development of new interfaces by providing a reusable and extendable code base. It supports the computation of energies, gradients, various couplings─like spin-orbit couplings, nonadiabatic couplings, and transition dipole moments─and other properties for an arbitrary number of states with any multiplicities and charges.
View Article and Find Full Text PDFAnalyst
September 2025
Department of Pharmaceutical Analysis, School of Pharmacy, Fujian Medical University, Fuzhou 350108, P. R. China.
: The objective of this study is to develop a straightforward and expeditious clinical detection method for meropenem. This study aims to introduce an innovative nanoenzyme design, thereby broadening the application of platinum nanomaterials in biological detection. It seeks to facilitate the portable detection of meropenem using commercial software.
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