Sentence examples for multiple clinical variables from inspiring English sources
The methodology used to determine GCR was chosen to integrate multiple clinical variables into a single rating to provide a clinically relevant assessment of response to therapy (e.g. to simulate clinical judgement in a practice situation).
Current methods to predict patients' perioperative morbidity use complex algorithms with multiple clinical variables focusing primarily on organ-specific compromise.
We further analyzed the correlation between DR5 expression and multiple clinical variables including gender, age at diagnosis, smoking status, tumor site, tumor size, tumor stage, histologic grade, node status, overall survival and disease free survival.
However, the relationship between the multiple clinical variables and postural control in FM is not known.
We propose an optimization approach to identify highly heritable components of a complex disease as a function of multiple clinical variables.
21, 22 Our findings suggest that the assessment of multiple clinical variables can be used by clinicians to assess mifepristone response and dosing in CS.
In order to evaluate the performance of statistical models combining multiple biomarkers and/or clinical variables, values of multiple biomarkers were inputted into the model from the appropriate method, and in each case the output (predicted value) was between 0 and 1, with 0 being cirrhosis and 1 being cancer.
Although case-mix systems use multiple clinical data, other variables such as social variables also influence people's health and care outcomes.
These data were also analyzed using Cox multivariate survival analysis to assess the influence of multiple clinical and cardiac variables on outcome.
Greater patient involvement in the decision-making process was significantly correlated with treatment by mastectomy after adjustment for multiple clinical and demographic variables.
This non-parametric approach is a useful alternative to traditional parametric methods like LR and PLR that automatically incorporates interactions between multiple biomarkers and/or clinical variables.
