Background/Objectives: The number of individuals with lower limb loss (LLL) is rising. Therefore, identifying the walking potential in individuals with LLL and prescribing adequate prosthetic systems are crucial. Various factors can influence participants’ walking ability, to different extents. The aim of the present study was to apply machine learning methods to develop a predictive mode. This model can assist rehabilitation and limb loss care teams in making informed decisions regarding prosthesis prescription and predicting walking ability in individuals with LLL. Methods: The present study was designed as a prospective cross-sectional study encompassing 104 consecutively recruited participants with LLL (average age 62.1 ± 10.9 years, 80 (76.9%) men) at the Medical Rehabilitation Clinic. Demographic, physical, psychological, and social status data of patients were collected at the beginning of the rehabilitation program. At the end of the treatment, K-level estimation of functional ability, a Timed Up and Go Test (TUG), and a Two-Minute Walking Test (TMWT) were performed. Support vector machines (SVM) were used to develop the prediction model. Results: Three decision trees were created, one for each output, as follows: K-level, TUG, and TMWT. For all three outputs, there were eight significant predictors (balance, body mass index, age, Beck depression inventory, amputation level, muscle strength of the residual extremity hip extensors, intact extremity (IE) plantar flexors, and IE hip extensors). For the K-level, the ninth predictor was The Multidimensional Scale of Perceived Social Support (MSPSS). Conclusions: Using the SVM model, we can predict the K-level, TUG, and TMWT with high accuracy. These clinical assessments could be incorporated into routine clinical practice to guide clinicians and inform patients of their potential level of ambulation.

Introduction. Pain perception in women may vary due to hormonal fluctuations across the phases of the menstrual cycle. Emotional status also tends to fluctuate significantly during these phases. This study aimed to investigate differences in pressure pain thresholds between the follicular and luteal phases of the menstrual cycle. An additional objective was to evaluate variations in emotional status. Material and Methods. The phases of the menstrual cycle were determined using an online ovulation calculator. Participants in the ovulation phase (n=5) were excluded from further analysis. A total of 95 participants (mean age 27.7 ? 7.8 years) were included in the study. Pressure pain threshold testing was conducted on the extensor carpi radialis longus muscle and the paraspinal musculature of the lumbar region using an algometer equipped with a 1 cm2 rubber tip. The Depression, Anxiety, Stress Scale was employed to evaluate variations in emotional status. Results. No significant differences were observed in pressure pain thresholds in the forearm region (follicular phase (33.55 ? 12.15 N/cm2) vs. luteal phase (33.55 ? 13.65 N/cm2), t=0.509; p=0.979) or the lower back region (follicular phase (56.85 ? 19.95 N/cm2) vs. luteal phase (58.93 ? 21.20 N/cm2), t=0.982; p=0.619) between the follicular and luteal phases of the menstrual cycle. Similarly, no significant differences were identified in levels of depression (?2 = 1016.000; p = 0.392), anxiety (?2 = 972.500; p=0.243), or stress levels (t = -1.038; p=0.302) during the menstrual cycle. Conclusion. The findings indicate no significant variations in pressure pain thresholds or emotional status across different phases of the menstrual cycle.