Statistical analysis
Infant characteristics were calculated for perinatal and social background factors. Normality of the data was examined by the Shapiro–Wilk test. Because the data were not normally distributed at both timepoints, we used Spearman’s rho (Because of the high degrees of correlation between the AIMS and the PDMS-2 found by other authors [Bland Altman plots were used to detect if there were systematic differences between the measurements or identify outliers. A Bland Altman plot gives a graphical presentation of the differences between two tests plotted against the average of the two tests. It visualizes the differences between two different measurements and shows the agreement with 95% confidence interval (upper and lower limit). If 95% of the scores is within these limits it shows high consistency. Furthermore, it gives a visual assessment of the scoring distribution and of potential measurement bias [Receiver-operating characteristics curves (ROC curves) were plotted to calculate the area under the curve (AUC) as an estimate of diagnostic accuracy of the AIMS and the different subscales of the PDMS-2 with respect to motor outcome at 24 months PTA. The 24-month motor outcomes were determined using the PDMS-2 total motor quotient, at either 1 standard deviation (SD) below the mean, or 2 SD below the mean of the normative sample. A perfect test has an AUC of 1.00, an AUC of 0.91 indicates excellent diagnostic accuracy, whereas 0.5 indicates discrimination no better than by chance [