This study aimed to determine differences in the validity and reliability of 1RM predictions made using load-velocity relationships in Smith machine and free-weight exercise. Twenty well-trained males attended six sessions, comprising the Smith machine and free-weight squat, bench press, prone row and overhead press. Load-velocity relationship-based 1RM predictions were performed using minimal velocity threshold (1RM), load at zero velocity (1RM) and force-velocity (1RM) methods, with 5- or 7-loads. Measured 1RM did not differ from 1RM or 1RM for any of the Smith machine exercises, while it was higher than 1RM for all exercises except the prone row. For the free-weight variations all 1RM predictions differed from measured 1RM for the squat and overhead press, while measured and predicted 1RM did not differ in the bench press and prone row. No differences were observed between 7-and 5-load predictions. 1RM was the most reliable and valid of the methods. Smith machine exercises resulted in more reliable predictions than free weight exercises. 1RM provides valid and reliable predictions for the Smith machine, squat, bench press, prone row and overhead press and free-weight bench press and prone row. Practitioners must be aware of the poor validity of free-weight squat and overhead press predictions.
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http://dx.doi.org/10.1080/02640414.2020.1794235 | DOI Listing |
Cancers (Basel)
December 2024
BC Cancer, Vancouver Center, 600 West 10th Avenue, Vancouver, BC V5Z 4E6, Canada.
Background/objectives: Pembrolizumab monotherapy is approved in Canada for first-line treatment of advanced NSCLC with PD-L1 ≥ 50% and no EGFR/ALK aberrations. However, approximately 55% of these patients do not respond to pembrolizumab, underscoring the need for the early intervention of non-responders to optimize treatment strategies. Distinguishing the 55% sub-cohort prior to treatment is a real-world dilemma.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania.
Importance: Recently, the US Food and Drug Administration gave premarketing approval to an algorithm based on its purported ability to identify individuals at genetic risk for opioid use disorder (OUD). However, the clinical utility of the candidate genetic variants included in the algorithm has not been independently demonstrated.
Objective: To assess the utility of 15 genetic variants from an algorithm intended to predict OUD risk.
Cancer Cell
December 2024
Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA. Electronic address:
Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.
View Article and Find Full Text PDFJ Bone Miner Res
January 2025
Departments of Medicine and Radiology, University of Manitoba, Winnipeg, Canada.
Vertebral fracture assessment (VFA) images from bone density machines enable the automated machine learning assessment of abdominal aortic calcification (ML-AAC), a marker of cardiovascular disease (CVD) risk. The objective of this study was to describe the risk of a major adverse cardiovascular event (MACE, from linked health records) in patients attending routine bone mineral density (BMD) testing and meeting specific criteria based on age, BMD, height loss, or glucocorticoid use have a VFA in the Manitoba Bone Mineral Density Registry. The cohort included 10 250 individuals (mean 75.
View Article and Find Full Text PDFPharmacotherapy
January 2025
Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, Georgia, USA.
Background: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given the high frequency and time-dependency of their use and other factors affecting FO. We sought to employ unsupervised machine learning methods to uncover medication administration patterns correlating with FO.
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