Childhood and Adolescent Lymphoma in Spain: Incidence and Survival Trends over 20Years

R. Marcos-Gragera,M. Solans,J. Galceran, R. Fernández-Delgado,A. Fernández-Teijeiro,A. Mateos, J. R. Quirós-Garcia, N. Fuster-Camarena, V. De Castro,M. J. Sánchez,P. Franch,M. D. Chirlaque,E. Ardanaz,C. Martos,D. Salmerón,R. Peris-Bonet, The Spanish Childhood Cancer Epidemiology Working Group

Clinical and Translational Oncology（2018）

Cited 6|Views37

Abstract

Lymphoma is the third most common malignancy in children (0–14 years) and the first in adolescents (15–19 years). This population-based study—the largest ever done in Spain—analyses incidence and survival of lymphomas among Spanish children and adolescents.

Translated text

Key words

Childhood and adolescent cancer,Lymphoma,Incidence,Survival,Population based,Cancer registry,Spain

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

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

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper