Catching Those Who Fall Through the Cracks: Integrating a Follow-Up Process for Emergency Department Patients with Incidental Radiologic Findings.

Tyler W. Barrett, Nicholas M. Garland,Clifford L. Freeman, Katharine Klar, Jan Dahlke,Penny Lancaster, Larry Prisco, Sam S. Chang,Laura W. Goff,Stephan Russ,Ian D. Jones

Mechanisms of Ageing and Development（2022）SCI 2区SCI 3区

Vanderbilt Univ

Cited 33|Views7

Abstract

The Strehler-Mildvan modification of the Gompertz relationship between aging and mortality provides a dynamic theoretical model for identifying and separating genetic, environmental and competitive influences upon age-related mortality. The initial method of longitudinal Gompertzian analysis, which utilized linear regression, tended to underestimate both genetic and environmental influences upon age-related mortality. A modified method of longitudinal Gompertzian analysis has been applied to mortality due to stomach cancer, cervical cancer and emphysema. This modified method of longitudinal Gompertzian analysis suggests that the genetic influence upon age-related mortality is essentially the same for both men and women. Moreover, application of this modified method suggests that environmental influences upon age-related mortality in the United States have been declining for the past 20 years for men and for the past 45 years for women.

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Key words

AGING,MORTALITY,EPIDEMIOLOGY,GOMPERTZIAN ANALYSIS

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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