Sentence examples for multivariable model identified from inspiring English sources

In 73 patients, who were followed prospectively with clinical visits for a median period of 8 years, a multivariable model identified baseline GM magnetization transfer ratio histogram peak height and average lesion magnetization transfer ratio percentage change after 12 months as independent predictors of disability worsening at 8 years (r = 0.28) [ 58].

The multivariable model identifies several subgroups that have a risk of recurrence that is significantly greater than 20% after a six month seizure-free period.

Multivariable modeling identified independent predictors of ICH.

Multivariable models identified older age, lower left ventricular ejection fraction, lower or higher body mass index, cerebrovascular disease, lack of internal mammary artery use, and lower cholesterol to be associated with increased risk of both events.

However, additional confirmatory analyses of the importance of the variables within the multivariable models identified utilising baseline data was undertaken using data from the control arms of this trial.

Rural and urban sample data were stratified a priori, and a purposeful selection strategy was used to select exploratory multivariable models identifying covariates that were independently associated with screening status.

The multivariable logistic regression model identified HFNC [OR 0.43 (0.18–0.99), p = 0.04) and cancer [OR 2.87 (1.04–7.91), p = 0.04] as independently associated with postextubation respiratory failure (see Additional file 1).

Our multivariable logistic regression model identified 4 independent risk factors for severe clinical disease (Table 3).

Adjusted multivariable logistic regression model identified year of diagnosis, re-excision and surgical treatment facility type to be independently associated with delay in first adjuvant therapy as well as for delay in chemotherapy and radiation therapy (Table  2).

A multivariable model is identified for the experimental flexible structure by using these orthonormal basis functions.

The following confounding variables for multivariable model were identified (P < 0.1): child gender, parental age, parental education, and for cognitive development additionally marital status and nursery attendance.