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T-assisted therapies), we still see sign differences inside the loadings across PCs, further indicating they may be diverse. Though future perform is necessary to establish how robust these trends stay inside the long-term, these results currently allow us to create our two key claims: the numerical robustness of distinctive rehabilitative trends in the short-term, and that that utilizing a single principal outcome because the sole criterion for clinical relevance fails--at the very least--to consider the multi-dimensional nature of short-term recovery. The limitations of PCA (see under) plus the heterogeneity of conventional therapy in this study are a limitation that, nevertheless, usually do not challenge our outcome thatrobot-assisted and traditional therapies each and every make diverse rehabilitative trends. In contrast to standard therapy, robotic therapy can--in principle--better standardize therapeutic delivery across sufferers and centres. This is tougher to achieve in multi-center, multitherapist delivery of traditional therapy. On the other hand, the robustness of our benefits as tested by the random shuffling of individuals into two groups (see Additional file 1), shows that the unavoidable variability introduced by, say, measurement error, inter-subject variations, or differences in conventional therapy across centres didn't wash out the distinction in rehabilitative trends between the traditional and robot-assisted therapies. Just before discussing the outcomes in detail, there are various methodological difficulties to think about offered that interpreting PCA requires a certain degree of analytical nuance. As with any dimensionality-reduction method [18], one should be cautious not to over-interpret the outcomes of PCA. We emphasize that our interpretation of the PCA results--and their robustness to information shuffling--pertains only for the demonstration that robot-assisted and standard therapies every single make distinct rehabilitative trends. Going beyond this to evaluate the number of PCs to consider, and to interpret their individual loadings, demands care. As towards the initially challenge, PCs are defined and listed in order of descending importance (i.e., percent of variance explained); and figuring out how lots of PCs to think about depends on the nature in the question. If one is enthusiastic about the amount of PCs essential to provide an equivalent--but lower dimensional or more compact--representation of the data, researchers inside the field of motor handle ordinarily use as several PCs as essential to clarify 60 to 80 of your variance in the data [18, 20]. Having said that, our aim right here is just to demonstrate that the dominant trends (i.e., PCs that suffice to account for 50 from the variance) are distinct across the two rehabilitation groups. Given that we're not creating an argument about the volume of dimensionality reduction, the first two PCs suffice to establish differences in the dominant trends. The 3rd to 7th PCs each naturally explains an more and decreasing level of variance in the final results, ranging from 16 to five . But interpreting them becomes increasingly unclear, and they are not important or useful to establish variations inside the dominant rehabilitative trends. Even though we refrain from discussing them in detail, the supplemental material further show that the five remaining PCs continue to show variations in between therapies--which reinforces our primary acquiring. As to the second concern, interpreting the loadings of each and every Computer have to be performed carefully.