Regressing an Optimal Variable to Scale Glomerular Filtration Rate
Clinical Nuclear Medicine(2014)
Abstract
Background To compare glomerular filtration rate (GFR) among individuals, GFR is usually scaled to body surface area (BSA) based on the ratio method, which has been debated for its accuracy in recent years. Reference to the BSA as a normalization standard is the most common method currently in use but has limitations. This study was designed to a better variable to scale GFR. Methods We measured 99mTc- diethylene triamine pentaacetic acid plasma clearance (uncorrected GFR, uGFR) for 322 healthy adults who were enrolled according to the SENIEUR protocol. The individuals were randomly grouped into A and B for regressing and validating the optimal variable, respectively. Nonlinear regression was performed against uGFR, and the selected independent variables were body weight, height, age, and sex. Results Among several tested models, the regression coefficients of weight-age formula (W-A) were in narrower 95% confidence interval (CI). The coefficient of determination of the regression line between W-A and uGFR, as an indicator to explain the percentage of variations of GFR, was higher than that of other variables in both groups. The coefficient of determination of the regression line between W-A and uGFR was 0.571, which was higher than that of BSA (0.203) or TBW (0.241). Conclusion The index variable, based on both body weight and age, has a better statistical relationship to uGFR and is a better variable to scale GFR in adults.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
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
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
Summary is being generated by the instructions you defined