Publications by authors named "J D Griffiths"

Background: Recovery from major surgery can be difficult to predict given the many factors involved in treating disease and restoring preoperative function. Postoperative recovery metrics such as length of stay, complications, and mortality are typically described. However, large data quantities for patient-reported recovery are scarce.

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Characterising patterns of genetic diversity including evidence of local adaptation is relevant for predicting and managing species recovering from overexploitation in the face of climate change. Red abalone (Haliotis rufescens) is a species of conservation concern due to recent declines from overharvesting, disease and climate change, resulting in the closure of commercial and recreational fisheries. Using whole-genome resequencing data from 23 populations spanning their entire range (southern Oregon, USA, to Baja California, MEX) we investigated patterns of population connectivity and genotype-environment associations that would reveal local adaptation across the mosaic of coastal environments that define the California Current System (CCS).

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Feeding and eating disorders (FEDs) are a heterogeneous grouping of disorders at the mind-body interface, with typical onset from childhood into emerging adulthood. They occur along a spectrum of disordered eating and compensatory weight management behaviors, and from low to high body weight. Psychiatric comorbidities are the norm.

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Freshwater waterways, and species that depend on them, are threatened by urbanisation and the consequences of the urban stream syndrome. In south-east Queensland, Australia, little is known about the impacts of the urban stream syndrome on the platypus (), meaning that populations cannot be adequately managed by conservation practitioners. The aim of this study was to determine how habitat and environmental variables, related to the urban stream syndrome, influenced platypus distribution across this region.

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As the field of artificial intelligence evolves rapidly, these hallmarks are intended to capture fundamental, complementary concepts necessary for the progress and timely adoption of predictive modeling in precision oncology. Through these hallmarks, we hope to establish standards and guidelines that enable the symbiotic development of artificial intelligence and precision oncology.

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