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 is the most common symptom of knee osteoarthritis. Until recently, this pain was referred to as nociceptive pain. However, the difficulties of pain management in knee osteoarthritis indicate the possible presence of a neuropathic component. The objective of this study was to determine how often neuropathic component is part of chronic pain in knee osteoarthritis. Material and Methods. The study included 417 patients with knee osteoarthritis. The patients were tested using the Neuropathic Pain (Douleur Neuropathique) 4 Questions and a Numeric Pain Rating Scale. Patients were divided into 2 groups. The first group included patients with a Neuropathic Pain 4 Questions score ? 4, and the second group with a score < 4. Results. The majority of patients included in this study were females (301, 72.2%), and most of the patients scored less than 4 in the questionnaire (231, 55.4%). Among the patients with a score ? 4, 144 (77.4%) were female, which is significantly higher compared to the group of patients who scored < 4, 157 (68%) (p = 0.037). There was no statistically significant difference in age between the two groups (p = 0.231). The current pain intensity, average pain, and maximum pain during the last 4 weeks were significantly higher in the group with a score ? 4 (p < 0.001). Conclusion. A significant number of patients with knee osteoarthritis had a neuropathic component of pain. There were significantly more women in the group with score ? 4, and this group also reported significantly higher current, as well as average and maximum pain during last 4 weeks than the other group.

Introduction. Osteoarthritis is the most common form of arthritis which affects millions of people worldwide and represents the leading cause of disability among the elderly. There is a substantial number of guidelines available for the treatment of knee osteoarthritis. The primary aim of this paper is to explore the distinctions and similarities between knee osteoarthritis treatment guidelines. The treatment is divided into core treatment and additional steps. Core treatment of knee osteoarthritis involves education of the patient, weight loss in the case of overweight patients and establishing an exercise program. First step of additional treatment of knee osteoarthritis consists of non-pharmacological (application of orthoses, lateral wedge insoles, assistive walking devices, therapeutic modalities, manual therapy, aquatic exercise, Tai chi) and pharmacological therapy (topical nonsteroidal anti-inflammatory drugs, topical capsaicin, paracetamol). Second step of additional treatment of knee osteoarthritis - pharmacological therapy should be considered if the first step didn?t show any significant results. It involves the use of oral nonsteroidal antiinflammatory drugs, intra-articular corticosteroid injections and viscosupplementation. Third step of additional treatment of knee osteoarthritis - last resort pharmacological therapy Pain occurring in knee osteoarthritis. can be partially caused by central sensitization. Because of that, use of duloxetine and tramadol may be considered. Fourth step of additional treatment of knee osteoarthritis-end stage treatment of knee osteoarthritis is reserved for the most severe patients. It includes total knee replacement surgery, and if it is not possible, treatment with strong opioids could be considered. Conclusion There are many possibilities in treatment of knee osteoarthritis. Unfortunately, there is often a lack of concordance between different guidelines. In these circumstances, treatment plans should be personalized, while comprehending potential risks and benefits.

Introduction/Objective. Burnout is a multidimensional syndrome that disproportionately affects healthcare professionals. While organizational and occupational factors have been extensively studied, the role of individual traits, such as personality, remains underexplored. This study aimed to examine the relationship between specific personality traits, sociodemographic variables, and occupational burnout among medical doctors in the Republic of Serbia. Methods. A cross-sectional, correlational study was conducted in 2024 using an online survey completed by 304 medical doctors. The instruments included a sociodemographic questionnaire, a shortened Big Five Plus Two personality inventory, and the Copenhagen Burnout Inventory (work-related scale). Data were analyzed using descriptive statistics, t-tests, and linear regression. Results. There were no significant gender differences in burnout levels (p = 0.958), nor differences based on communication skills training (p = 0.214). However, participants who reported insufficient time for patient care had significantly higher burnout levels (p < 0.001). Linear regression analysis indicated that extraversion negatively predicted burnout (β = -0.24, p = 0.003), while conscientiousness (β = 0.21, p = 0.002) and openness (β = 0.12, p = 0.024) were positive predictors. The model was significant (R = 0.307, F(5,298) = 6.18, p < 0.001), explaining 9.4% of the variance in burnout. Conclusion. Extraversion appears to serve as a protective factor against burnout, whereas higher levels of conscientiousness and openness are associated with increased burnout. These findings highlight the relevance of personality traits in burnout susceptibility and suggest the potential for targeted interventions in physician support programs.