A Longitudinal Analysis Comparing the Mental Health of Children By Level of Young Carer Status.

Introduction: Young carers research has predominantly focused on the experiences of children who often provide substantial levels of care for family members, and the impacts of this caring on their lives. While quantitative studies of prevalence have increased, there have been increasing calls for cross-sectional and longitudinal studies of young carers relative to children without caring responsibilities, to strengthen and challenge the existing evidence on impact.

Methods/materials: The study utilized the Longitudinal Study of Young People in England: Next Steps (LSYPE), a cohort study of over 12,500 children aged 13 in 2004.

Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images.

Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the SIIM-ISIC Melanoma Classification Challenge; however, these solutions leverage ensembles of complex deep neural architectures requiring immense storage and computation costs, and therefore may not be tractable. A recent movement for TinyML applications is integrating Double-Condensing Attention Condensers (DC-AC) into a self-attention neural network backbone architecture to allow for faster and more efficient computation.

Androgen receptor pathway inhibitors and taxanes in metastatic prostate cancer: an outcome-adaptive randomized platform trial.

ProBio is the first outcome-adaptive platform trial in prostate cancer utilizing a Bayesian framework to evaluate efficacy within predefined biomarker signatures across systemic treatments. Prospective circulating tumor DNA and germline DNA analysis was performed in patients with metastatic castration-resistant prostate cancer before randomization to androgen receptor pathway inhibitors (ARPIs), taxanes or a physician's choice control arm. The primary endpoint was the time to no longer clinically benefitting (NLCB).

An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study.

Background: Comprehensive models of survivorship care are necessary to improve access to and coordination of care. New models of care provide the opportunity to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer treatment.

Objective: This paper presents our expert-informed, rules-based survivorship algorithm to build a nurse-led model of survivorship care to support men living with prostate cancer (PCa).