98%
921
2 minutes
20
Australia uses the International Classification of Diseases (ICD-10) for mortality coding and its Australian Modification, ICD-10-AM, for morbidity coding. The ICD underpins surveillance (population health, mortality), health planning and research (clinical, epidemiological and others). ICD-10-AM also supports activity-based funding, thereby propelling realignment of the foci of clinical coding and, potentially, coded data's research utility. To conduct a scoping review of the literature exploring the use of ICD-10 and ICD-10-AM Australian-coded data in research. Research questions addressed herein: (1) What were the applications of ICD-10(-AM) Australian-coded data in published peer-reviewed research, 2012-2022? (2) What were the purposes of ICD-10(-AM) coded data within this context, as classified per a taxonomy of data use framework? : Following systematic Medline, Scopus and Cumulative Index to Nursing and Allied Health Literature database searches, a scoping literature review was conducted using PRISMA Extension for Scoping Reviews guidelines. References of a random 5% sample of within-scope articles were searched manually. Results were summarised using descriptive analyses. Multi-stage screening of 2103 imported articles produced 636, including 25 from the references, for extraction and analysis; 54% were published 2019-2022; 50% within the largest five categories were published post-2019; 22% fell within the "Mental health and behavioural" category; 60.3% relied upon an ICD-10 modification. Articles were grouped by: research foci; relevant ICD chapter; themes per the taxonomy; purposes of the coded data. Observational study designs predominated: descriptive (50.6%) and cohort (34.6%). Researchers' use of coded data is extensive, robust and growing. Increasing demand is foreshadowed for ICD-10(-AM) coded data, and HIM-Coders' and Clinical Coders' expert advice to medical researchers.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10777611 | PMC |
http://dx.doi.org/10.1177/18333583231198592 | DOI Listing |
J Am Coll Health
September 2025
Department of Epidemiology and Community Health, College of Health and Human Services, The University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
Despite alarming rates of students' food insecurity in the US (41%), estimates may not be fully capturing experiences in university settings. Understanding students' food insecurity is a knowledge gap flagged amidst outstanding progress on food security measurement in household settings. This study investigated the domains shaping the experiences around food with implications for food insecurity among students.
View Article and Find Full Text PDFJ Neurophysiol
September 2025
Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Repetition suppression, the reduced neural response upon repeated presentation of a stimulus, can be explained by models focussing on bottom-up (i.e. adaptation) or top-down (i.
View Article and Find Full Text PDFPhys Rev Lett
August 2025
California Institute of Technology, TAPIR, Division of Physics, Mathematics, and Astronomy, Pasadena, California 91125, USA.
In the gravitational-wave analysis of pulsar-timing-array datasets, parameter estimation is usually performed using Markov chain Monte Carlo methods to explore posterior probability densities. We introduce an alternative procedure that instead relies on stochastic gradient-descent Bayesian variational inference, whereby we obtain the weights of a neural-network-based approximation of the posterior by minimizing the Kullback-Leibler divergence of the approximation from the exact posterior. This technique is distinct from simulation-based inference with normalizing flows since we train the network for a single dataset, rather than the population of all possible datasets, and we require the computation of the data likelihood and its gradient.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.
Biology has been transformed by the rapid development of computing and the concurrent rise of data-rich approaches such as, omics or high-resolution imaging. However, there is a persistent computational skills gap in the biomedical research workforce. Inherent limitations of classroom teaching and institutional core support highlight the need for accessible ways for researchers to explore developments in computational biology.
View Article and Find Full Text PDF