## 6 Results and Discussion
### Performance of the State of the Art Models  
To perform a fair comparison between our model (SL early-term) and five state of the art models (PRESTO-1, PRESTO-2, EVENT, Konigstein, and GRACIA-3), we designed two sets of experiments: (1) _test-only_ using the pre-trained model on our database only for testing, and (2) _retrained_ where all models are retrained and tested on our database using a 5-fold cross-validation approach. We repeated both sets of experiments to predict both TLR and TLF, separately. By doing so, we can demonstrate the performance of all models regardless of the particularity of their cohorts, features, and outcome of interest. Figure 4: Prediction performance of our ML models. Values shown in legends are areas under the curve and their 95% confidence interval for (**left**) ROC curve and (**right**) precision–recall (PR) curve. F-statistics show no significant difference between predictions. Figure 5: Retrospective and prospective validation of our TLF prediction model. Table 5 presents the list of model parameter values (\(\beta\)-coefficients for the three logistic regression-based models) as were reported in the literature by the respective authors (_test-only_), and after retraining on our database (_retrained_). The outcome of interest here is TLR, similar to the original study. As shown, EVENT has only one parameter overlap with the two PRESTO studies. When a given parameter did not exist in our database, we denoted it with NA, which appears only three times (two variables of EVENT and one variable of PRESTO-2). Hence, our database can be used to meaningfully compare these models. For the _test-only_ experiment, when a variable is not available in our database (NA), we impute it by sampling from a Bernoulli distribution with a success probability equal to 50%. Table 5 shows that \(\beta\)-coefficients are different between _test-only_ and _retrained_ experiments. Since the classifier is the same between these three models, we argue that the differences among the trained \(\beta\)-coefficients are most probably caused by the difference in cohort distributions and characteristics. For instance, if an outcome and a feature are confounded by a hidden variable whose distribution is different among the cohorts, the corresponding coefficient of that feature would differ between cohorts. For the same reason, it is important to note that these coefficients must be interpreted with care, especially when the goal is to find causal relations between the features and the outcome. For the retrained LR model, we also report the model intercept (Table 5) which has not been reported in other studies. The term intercept in an LR model gives the prior odds of the outcome when all of the features have zero values, or are missing. The intercept can be interpreted as a characteristic of the cohort. A high intercept value hints toward a bias in the model preferring to assign a positive prediction to patients with many missing features. We have reported the performance (AUC-ROC and precision score corresponding to 90% recall score) of these models for predicting TLF and TLR in Table 6. The results under the _test-only_ and the _retrained_ columns have been generated by using the original model