Sentence examples for multivariable models evaluated from inspiring English sources

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Multivariable models evaluated the independent predictive value of LGE above other clinically and statistically significant covariates.

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The multivariable model evaluated predictors associated with injuries caused as a result of contact with law enforcement personnel.

In a first multivariable model evaluating characteristics of sites and specific biosecurity variables, four main effects were significantly associated (P < 0.05) with PRRSV positive status: large pig inventory (OR: 10.7), proximity to closest pig site (OR: 7.3), absence of shower (OR: 8.7) and free access to the main entrance of the site by the rendering truck (OR: 7.0).

Topics of epidemiology (P < 0.001, OR = 0.653) and infections (P = 0.006, OR = 0.721) were significant in the multivariable model evaluating 13 topics.

Finally, after stepwise variable selection, tumour origin and perineural infiltration did not remain in the final multivariable model evaluating histopathologic factors of pancreatobiliary differentiated periampullary adenocarcinomas, in accordance with the results from the study group analysis.

Therefore, we sequentially added abnormal eGFR and ACR, elevated hsCRP, and all three variables to the multivariable model, evaluating the changes in the hazard ratio for the association between Cyst-C and first sepsis event.

Bivariable and multivariable logistic regression models evaluated associations of MVCT participation with socio-demographic, economic, and HIV risk characteristics, as well as HIV testing histories, HIV related stigma, and testing-related fears.

No significant interaction terms were found in the multivariable logistic regression models evaluated (p>0.05).

The predictive power of the final multivariable models was evaluated using sensitivity, specificity, positive predictive value, and the area under the receiver operating characteristic curve.

The predictive ability of the multivariable models was evaluated using the concordance (c) index.

A multivariable regression model evaluated these factors as independent predictors of remission.

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