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Evaluating and improving a model of nursing care is a fundamental part of clinical practice improvement.
Improving a model that is already considered as good, is difficult because only very strong predictors may result in a significant improvement, while other moderately strong predictors do not affect the model [ 30].
Recalibration to the incidence of acute kidney injury within a particular hospital can be a useful means of improving a model's predictive performance.
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First, as Cartwright (1989) points out, there is no reason to assume that one can always improve a model by adding de-idealizing corrections.
To improve a model developed in Comsol Multiphysics for assessing the hygrothermal performance of rammed earth constructions, flexible dynamic small-scale experiments are presented.
Temperature and static solution tools associated with the FE method can facilitate the incorporation of more experimental data into the FE models (i.e., local measurements of modulus from micro- or nano-indentation) and perhaps improve a model's performance.
New experimental results improve a model and a model can guide the next round of experiments.
Inclusion of covariates can improve a model's accuracy and precision in mortality estimates.
It may improve a model's utility by removing some values and reducing the number of attributes.
It was reported that inclusion of AOD and meteorological fields can significantly improve a model's predictability of surface PM2.5 concentrations [ 19, 21].
Recent attempts to construct and improve a model host for the heterologous expression of genes encoding secondary metabolites have done so by deleting nonessential genes [ 9, 10].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com