Chrome Extension
WeChat Mini Program
Use on ChatGLM

Regional Disparities in Ovarian Cancer in the United States.

Cancer health disparities(2019)

Division of Preventive Medicine | Division of Gynecology Oncology | University of Liverpool | Department of Biostatistics | Division of Biostatistics and Bioinformatics

Cited 1|Views10
Abstract
The aim of this study was to investigate the association between geographic regions and ovarian cancer disparities in the United States. Data from the Surveillance, Epidemiology, and End Results (SEER) Program was used to identify women diagnosed with ovarian cancer. 18 registries were divided into two groups: South region and US14 region. Chi-Square tests were used to compare proportions, the logistic regression model to evaluate the association between 5-year survival and other variables, and the Cox proportional hazards model to estimate hazard ratios. The South region had a lower incidence rate than the US14 region (12.0 vs. 13.4 per 100,000), and a lower 5-year observed survival rate (37.5% vs. 39.8%). White women living in the US14 region had the best overall survival, compared to white women living in the South region, and black women living in both regions. Women in the South region were less likely to have insurance (6.6% vs. 2.7%, p<0.0001) and surgery (73.4% vs. 76.2%, p<0.0001). Women living in the South were 1.4 times more likely to die after five years of diagnosis than women living in the US14 region. The data confirmed regional disparities in ovarian cancer in the United States, showing women living in the South region were disadvantaged in ovarian cancer survival regardless of race, black or white. Future research focusing on the identification of contributing factors to regional disparity in ovarian cancer is necessary to develop practical approaches to improve health outcomes related to this lethal disease.
More
Translated text
求助PDF
上传PDF
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
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
L.P. Shulman
2010

被引用111 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest