# Are Electromyography Data a Fingerprint for Patients with Cerebral Palsy (CP)?
## 4 Discussion
In the present study, strict inclusion criteria were applied to ensure a more homogeneous surgical approach, focusing on patients who underwent soft tissue surgeries on muscles located at the shank and foot level, among other procedures. The observed differences between our results and those in the literature may stem from this restrictive inclusion criterion, which distinguishes our population from other studies. We propose that the improvement in the plantar flexion-knee extension coupling mechanism, driven by better activation patterns in the soleus and rectus femoris, is likely the primary contributor to the observed improvements in gait post-surgery for this population. It appears that other muscles, such as the tibialis anterior, gastrocnemius, and hamstrings, did not significantly contribute to this improvement. The examined EMG indices, EDI and EPS, basically describe EMG patterns and show a tendency to approach typically developed (TD) levels, as indicated by a reduction in ND (Table 3), corresponding to improvements in gait following surgery (gait parameters, Table 2). This might be interpreted as a biomechanical aspect of EMG activity. In contrast, DMC seems to be a quantity that is relatively independent of the changes in gait patterns or indexes derived from them (Table 3). Therefore, and this is the second hypothesis we examined in this study, DMC can be a partial predictor of outcomes: 'Good DMC = good response; Poor DMC = poor response' (i.e., of orthopedic/biomechanical intervention). According to [21], we applied a clustering algorithm to divide the subjects into groups of patients with similar gait quality in E1 and the same response to the treatment in E2. Considering the possible recovery conditions, DMC was the only measure that showed a difference between the responding groups with the same baseline GDI (Tables 5-7), while it remained the same from E1 to E2. The patients with a relatively low DMC (\(\approx\)70 out of 100 for TD) along with poor gait quality (GDI \(\approx\) 40 out of 100 for TD) are more likely to have worse outcomes after treatment (Table 5). This finding interestingly implies that orthopedic surgeons should be cautious not to overtreat patients with severely limited motor control. Conversely, a relatively high DMC (\(\approx\)80 out of 100 for TD) for patients with a better pre-treatment GDI (\(\approx\)70 out of 100 for TD) can lead to further improvement in their gait following the intervention (Table 7). Moreover, for mild cases (Table 6), it can also be seen that the average DMC for the good responders was higher than for the other groups. However, for these cases, it might be difficult to distinguish between the biomechanics and the motor effect on their gait deficit. It should be noted that DMC was the only measure in our study that significantly differentiated between responders, particularly in two groups: (1) poor responders with worse pre-treatment gait quality and (2) good responders with relatively high baseline GDI. While the averages presented in Tables 5-7 demonstrate that DMC differs between responders and has the potential as a predictor in clinical decision-making, our final conclusions are statistically valid only for these two specific responder groups. It is crucial to acknowledge that the descriptive terms used in this study, such as poor, good, mild, better, and worse, are context-specific and pertain to the population included in our research. In other clinical settings, where the severity levels of patients may differ, these thresholds might not apply uniformly. Nevertheless, the inclusion of 167 patients in our study provides a sufficiently large sample size to generalize the systematic changes in EMG post-surgery, validate the application of EMG indices in clinical practice, and underscore the significance of DMC as a measure of motor control function in clinical decision-making.