## 2 Materials and Methods
### Study Implementation  
Both tests, the GMFM-66, and the rGMFM-66, were conducted under consistent conditions to ensure comparable testing environments. The assessments took place in similar rooms and at similar times of day, ensuring that external factors such as fatigue and exhaustion were minimized. All assessments were conducted at the Center for Prevention and Rehabilitation of the University of Cologne. Additionally, the testing process followed the same internal standardized manual, with therapists using identical verbal cues and instructions as outlined in the manual. This adherence to standardized procedures ensured that both assessments were conducted under equal conditions, minimizing variability and allowing for an accurate comparison of the two test protocols.  
As a first step, the rGMFM-66 uses an AI-driven algorithm to classify children's motor abilities into three groups (low, medium, and high) by analyzing 7 pre-defined test items. Depending on the group, the AI algorithm selects 15 to 33 relevant test items from the GMFM-66, ensuring that children with lower abilities are tested with fewer, focused items, while those with higher abilities complete a broader range. Within each group, the same predefined set of items is used for all children, rather than selecting individual items for each child. The rGMFM-66 generates a score directly comparable to the GMFM-66. The prediction of group membership (low, medium, high GMFM-66 score) based on the 7 pre-defined test items is performed using an artificial neural network, and the prediction of the rGMFM-66 is based on a support vector machine model. In summary, the complete data set of the previous publication describing the generation of the rGMFM-66 algorithm (1217 GMFM-66 assessments) was split into a training set (\(\approx\)two-thirds) and a validation set [13; 17]. The GMFM-66 score, based on all 66 items, was used to divide the training data into three groups (low, medium, and high GMFM-66 scores). A random forest (RF) model was applied within each group to assess the importance of individual GMFM-66 test items for predicting the overall score. Iteratively, the least important item was removed, and the model's mean squared error (MSE) was recalculated, repeating this process until only 16 test items remained. This allowed us to identify the maximum number of items that could be excluded without significantly increasing the MSE, resulting in one reduced item set per group (low, medium, high). Since the correct group was unknown prior to assessment, the intersection of all three reduced sets (7 items in total) was used to predict the likely group using three machine learning models (Random Forest, Support vector machine, and feed-forward neural net). The selection of these 7 items was fully data-driven and based on their consistent predictive strength across all three subgroups. The items were not chosen manually but selected by an automated machine learning process using feature importance analysis from random forest models. The corresponding reduced item set could then be selected accordingly. This is only a brief description of the complex creation process of the rGMFM-66 algorithm, as it is not the subject of this study. However, a detailed description, including the exact tuning process with the optimal hyperparameters of the various prediction models tested, is given in the two publications mentioned above. Examples of the algorithmic item selection process and its clinical application are illustrated in the Supplementary Materials of [PERSON] et al. (2022), which the present study builds upon [13; 17]. The software for the rGMFM-66 can be obtained by requesting it from the corresponding author.