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.

Introduction. Osteoarthritis is the most prevalent joint disease worldwide, with knee osteoarthritis representing its most common form. Pain in knee osteoarthritis is known to have neuropathic components; however, the mechanisms leading to central sensitization remain incompletely understood. It is also unclear whether surgical pain relief achieved through total knee arthroplasty results in modulation of central sensitization and neuropathic pain. Quantitative sensory testing in the prediction of postoperative pain after total knee arthroplasty. Sensory profiling using quantitative sensory testing modalities - particularly temporal summation and conditioned pain modulation - has emerged as a valuable approach for identifying patients with osteoarthritis who are at risk of acute and chronic postsurgical pain. These modalities, when combined with other parameters, may facilitate individualized pain management strategies. Preoperative hyperalgesia and neuropathic pain features, detected through temporal summation testing and Pain- DETECT questionnaires, have demonstrated predictive value for the development of chronic postsurgical pain after total knee arthroplasty. Patients exhibiting centrally mediated pain mechanisms and established central sensitization tend to experience less postoperative pain relief following total knee arthroplasty. Female sex has been associated with higher magnitude of conditioned pain modulation and an increased prevalence of severe acute and chronic postsurgical pain, identifying it as a potential risk factor for pain chronification. Conclusion. Quantitative sensory testing modalities - particularly temporal summation, conditioned pain modulation, and pressure pain threshold - appear to be the most promising tools for predicting postoperative pain outcomes using quantitative sensory testing. Patients with evidence of central sensitization show reduced pain relief after total knee arthroplasty.

Introduction. Flexibility and joint mobility are fundamental determinants of efficient and safe movement execution. They play an important role in injury prevention during daily activities, training, and competitive performance, and substantially influence athletic outcomes. Between the ages of 20 and 49, flexibility declines by approximately 10% per decade. Stretching is one of the primary interventions used to restore, maintain, and enhance flexibility. Evidence suggests that regular and structured stretching programs can preserve joint range of motion and improve mobility. Stretching has also been proposed to enhance physical performance, facilitate post-exercise recovery, and reduce injury risk. Discussion. Stretching constitutes an essential component of both athletic training and rehabilitation; therefore, identifying the optimal timing, volume, and intensity of stretching within motor learning and re-education programs is of considerable importance. However, despite widespread application, the scientific literature has not yet reached a clear consensus regarding its effects, as evidence remains inconsistent or insufficient for several commonly cited benefits. The aim of this narrative review is to summarize current evidence on the effects of dynamic and static stretching on injury prevention and physical performance. Conclusion. It remains difficult to definitively determine whether stretching exerts predominantly beneficial or adverse effects. Given the heterogeneity and often contradictory findings in the existing literature, stretching protocols should be individualized, with careful consideration of the type, volume, and intensity of stretching in relation to the specific goals of rehabilitation or athletic training.