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In each model we introduced age, gender, BMI, education, personal history of other disease into model and built the final model with a backward stepwise approach.
We built the final model using a forward stepwise approach, adding each factor significant at the level of 0.1 in the model one by one.
After the prior selection of variables, based on the findings of the previous steps, we built the final model to discriminate the rheumatologist's decision, which was only slightly better than the DAS28.
To assess the independent effect of the intervention on VCT, using a backward procedure based on the likelihood ratio test with a p-to stay = 0.05, we built the final model, entering all possible confounders previously identified and correlates with a p value <0.20 [ 17].
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The value that makes the model perform the best is then used to build the final model.
The combination of Support Vector Machines (SVM) classifiers is used to build the final model to generate comprehensive perception.
The optimum number of components (ONC) was used for the non-cross validation PLS analysis to build the final model with the corresponding conventional correlation coefficient (r2), standard error of estimate (SEE and the F value.
The risk factors remaining to build the final model for the drag swabs outcome were introduced into the basic model as fixed effects factors all at once.
As residues 1 40 of Ets-1 are intrinsically disordered a two-step strategy was adopted to build the final model for the ERK2Ets complex.
To build the final model the two models (A and B) were combined by first aligning them and then combining residues 1-39 from the best ranked Model B with residues 40-138 froModelel A (Fig. 3B).
To build the final model for each sex, we used the following modeling strategy.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com