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The multivariable model included deep bedding and herd size, and explained 59% of the variation of severe lameness.
The multivariable model included herd size, access to pasture, and provision of deep bedding, and explained 50% of the variation in clinical lameness.
The multivariable model included herd size, percentage of stalls containing fecal contamination, and presence of rubber in the alley to the milking parlor, and explained 44% of the variation of clinical lameness.
This is likely because our multivariable model included femoral head diameter, which is more intrinsically correlated with cortical distance, than is the choice of stem size per se.
The estimate was barely insignificant when the multivariable model included only significant covariates from the univariate step (coefficient: –0.00046, 95% CI: –0.00095, 0.00003, p = 0.063).
The multivariable model included covariates found to be statistically significant (p<0.05) on unadjusted analysis as well as those deemed a priori to be most biologically salient to CIN incidence and progression.
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According to these results, we built a multivariable model including the SOFA score, INR, and RRT.
A multivariable model including exercise ejection fraction and clinical history variables provided slightly more prognostic information than the combination of cardiac catheterization and clinical data.
In a multivariable model including all study patients, the ICD reduced mortality (hazard ratio [HR] 0.65, 95% posterior credibility interval [PCI]) 0.40-0.99).
In a multivariable model including resting HR and CI as continuous variables, neither was associated with the primary outcome of all-cause mortality or hospitalization.
We aimed to build a multivariable model including the best general ICU score, the best biological variable of hepatic failure, and the most relevant therapy performed at ICU admission.
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