## 4 Discussion  
The results of this study show that it is possible to predict the GMFM-66 score based on gait analysis with the Zebris FDM pressure plate in children with CP (and GMFCS levels I and II), achieving at best a CCC of 0.49 (95% CI 0.31-0.63), an MAE of 6.32 (95% CI 5.35-7.28) and an RMSE of 7.59 (95% CI 6.56-9.02) using the XGBoost model (a machine learning algorithm, see Table 4). Although the XGBoost model showed the best predictive performance among the five tested models, the low CCC indicates only moderate agreement between the predicted and the true GMFM-66 scores (CCC: 0.31-0.63). XGBoost is widely regarded as one of the most effective machine learning algorithms for structured, tabular data. Its success stems from its ability to efficiently capture complex feature interactions and nonlinearities through gradient-boosted decision trees, while also incorporating regularization to reduce overfitting [42]. Compared to traditional random  
Figure 6: Bland–Altman diagrams of the XGBoost model. The results of the Bland–Altman analysis are presented. The dashed lines represent the mean difference and the limits of agreement (1.96 \(\times\) standard deviation). Each circle corresponds to a prediction of the GMFM-66. Figure 7: Feature importance of the XGBoost model. forests, XGBoost typically achieves higher predictive accuracy due to its additive model structure and optimization techniques. SHAP values were also calculated for the XGB model, revealing that the three most important predictors were step length, variability in stance phase duration and foot rotation. To our knowledge, no prior study has specifically predicted the GMFM-66 score based on gait analysis with ground reaction force plates. To date, only one study has investigated GMFM score prediction using instrumented gait analysis (IGA): [PERSON] et al. used the Zebris FDM-T treadmill and generalized linear regression to predict the GMFM-88 (not GMFM-66) score in 23 patients, achieving a median RMSE of 6.9 for the total score--only slightly higher than the RMSE of our XGBoost model [25]. [PERSON] et al. investigated the correlations between the modified Gait Deviation Index (mGDI) derived from 3D kinematic gait analysis and the GMFM-66 in \(n=173\) children with CP and GMFCS levels I-IV. They reported a significantly higher correlation coefficient between mGDI and GMFM-66 (0.7) than that observed in our study (0.44, 95% CI 0.24-0.60) [24]. The mGDI is based on values from 3D kinematic measurements [20], which were not available for the participants in our study. It is therefore plausible that including kinematic data could improve prediction accuracy. In addition to measurements of the lower extremities and pelvis, thorax and spine movements could also be considered. [PERSON] et al. found significant differences across CP subtypes and GMFCS levels: children with higher GMFCS levels exhibited greater thoracic movements, and children with diplegia and GMFCS level 3 showed greater ranges of motion in all directions except spinal rotation [43]. Since the Zebris FDM system allows for kinematic and EMG measurements, future investigations including these parameters in regression models could test this hypothesis. To determine whether GMFM-66 prediction via IGA is clinically meaningful for evaluating treatment outcomes, minimum clinically important differences (MCIDs) must be considered. The MCID represents the threshold beyond which a change is deemed clinically relevant. [PERSON] et al. described MCIDs necessary to recognize visible functional improvements (measured by effect size, ES) in a longitudinal observational study of \(n=381\) children with CP and GMFCS levels I-III. They postulated that changes must reach at least medium ES (0.5-0.8) or large ES (>0.8) to be clinically relevant. According to [PERSON] et al., at GMFCS level I, a GMFM-66 score change of 1.7 or more (over one year) constitutes a medium ES MCID, and a change of 2.7 or more constitutes a large ES MCID.