EXPERIENCES OF USING THE THEORETICAL DOMAINS FRAMEWORK ACROSS DIVERSE CLINICAL ENVIRONMENTS: A QUALITATIVE STUDY

Experiences of using the Theoretical Domains Framework across diverse clinical environments: a qualitative study

Cameron J Phillips,1,2 Andrea P Marshall,3,4 Nadia J Chaves,5 Stacey K Jankelowitz,6,7 Ivan B Lin,8 Clement T Loy,9,10 Gwyneth Rees,11 Leanne Sakzewski,12 Susie Thomas,13,14 The-Phung To,15 Shelley A Wilkinson,16,17 Susan Scrubs/Scrapers Michie18 1Division of Pharmacy, Flinders Medical Centre, Bedford Park, SA, Australia; 2School of Medicine, Flind

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Preoperative Prediction and Risk Factor Identification of Hospital Length of Stay for Total Joint Arthroplasty Patients Using Machine Learning

Background: The aim of this study was to improve understanding of hospital length of stay (LOS) in patients undergoing total joint arthroplasty (TJA) in a high-efficiency, hospital-based pathway.Methods: We retrospectively reviewed Muslins 1401 consecutive primary and revision TJA patients across 67 patient and preoperative care characteristics fro

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xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data

Abstract Background Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or Dryer Vent Cover environmental exposures.Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection

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