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We applied each of the four derived models to its corresponding validation cohort, using the coefficients from the derivation models.
The AUC for the derivation models ranged from 0.64 to 0.73 (Additional file 1).
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For each model, we calculated the same indices for assessing model performance [19] as in the derivation model.
Specifically, we used the coefficients estimated from the derivation model to predict the log-odds of mortality in the validation cohorts.
All variables in the derivation model were retained in the bootstrapped model (Table 4).
Based on the derivation model, the probability for having breast cancer in the validation cohort was used to divide subjects into deciles.
Second, the model requires validation in an external dataset; however, an internal validation with bootstrapping was applied to the derivation model.
We used the bootstrapping method to derive robust estimates of the standard errors of the odds ratios of the variables in the derivation model.
If relevant differences are found between the derivation and validation cohorts, a cohort-specific intercept and/or interaction term may be included in the final model; otherwise, it will maintain the same risk factors and form as the derivation model.
The confidence intervals estimated by the bootstrapped samples showed that the variables in the derivation model appropriately reflect the statistically independent predictors of the probability of readmission to the CVICU.
However, this would require the variance-covariate matrix of β ^, something which is rarely if ever reported and something which would extend the level of information needed about the derivation model.
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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