Model validation
DISEASE
The presence of each predictor variable from the different models was evaluated in the ERNIE2 database. If variables were similar, but not exactly corresponding, a proxy was used. Missing values were considered non-deviant (see the ‘To assess the discriminative ability of the multinomial LRMs, Feverkidstool and Craig model, we calculated the conditional pairwise concordance statistic, hereafter referred to as the C-statistic, using the original reported coefficients and intercepts. The conditional risk was calculated by dividing the probability of the disease of interest by the sum of the probabilities of the disease of interest and of the absence of SBI. The C-statistic and its 95% confidence interval (CI) were calculated as the area under the receiver operating characteristic curve (AUC) of this conditional risk with the R statistical software version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria) [For the binomial SBI model, discrimination and calibration were evaluated using the val.prob.ci.2 function, an adaptation of the rms package [For the risk scores SBI score and PAWS, sensitivity, specificity, positive (LR( +)) and negative likelihood ratio (LR( −)) and the respective 95%-CIs were calculated at the cut-offs proposed by the original authors.