Background: Over one billion people worldwide suffer from neurological conditions that cause mobility impairments, often persisting despite rehabilitation. Chronic neurological disease (CND) patients who lack access to continuous rehabilitation face gradual functional decline. The International Classification of Functioning, Disability, and Health (ICF) provides a comprehensive framework for assessing these patients. Objective: This study aims to evaluate the outcomes of a non-hospitalized neuromotor rehabilitation project for CND patients in Italy using the Barthel Index (BI) as the primary outcome measure. The rehabilitation was administered through an Individual Rehabilitation Plan (IRP), tailored by a multidisciplinary team and coordinated by a physiatrist. The IRP involved an initial comprehensive assessment, individualized therapy administered five days a week, and continuous adjustments based on patient progress. The secondary objectives include assessing mental status and sensory and communication functions, and identifying predictive factors for BI improvement using an artificial neural network (ANN). Methods: A retrospective observational study of 128 CND patients undergoing a rehabilitation program between 2018 and 2023 was conducted. Variables included demographic data, clinical assessments (BI, SPMSQ, and SVaMAsc), and ICF codes. Data were analyzed using descriptive statistics, linear regressions, and ANN to identify predictors of BI improvement. Results: Significant improvements in the mean BI score were observed from admission (40.28 ± 29.08) to discharge (42.53 ± 30.02, p < 0.001). Patients with severe mobility issues showed the most difficulty in transfers and walking, as indicated by the ICF E codes. Females, especially older women, experienced more cognitive decline, affecting rehabilitation outcomes. ANN achieved 86.4% accuracy in predicting BI improvement, with key factors including ICF mobility codes and the number of past rehabilitation projects. Conclusions: The ICF mobility codes are strong predictors of BI improvement in CND patients. More rehabilitation sessions and targeted support, especially for elderly women and patients with lower initial BI scores, can enhance outcomes and reduce complications. Continuous rehabilitation is essential for maintaining progress in CND patients.

Rehabilitation after rotator cuff repair is crucial for functional recovery and for minimizing the risk of retear. There are two rehabilitation protocols (early and traditional) and the debate about which is the best is open. This umbrella review aimed to compare the effect of these rehabilitation protocols in terms of reduction in pain, functional recovery, and retear risk. We selected systematic reviews and meta-analyses published between 2012 and 2022 dealing with the aim. Nineteen systematic reviews were included. No significant differences were found between early and traditional protocols in terms of pain reduction. Early rehabilitation provided better short-term results regarding Range of Motion improvement, but long-term functional outcomes were similar. Retear risk remains a significant concern for the early protocol. We found major differences between the analyzed protocols. This review suggests that both protocols are useful to recover global shoulder function, but the standard protocol has a greater safety profile for larger tears. On the other hand, the early protocol may be preferable for smaller lesions, allowing a faster recovery and having less impact on medical costs. Further research is needed to identify optimal rehabilitation strategies tailored to the individual patient’s needs and characteristics.

Background: Chronic lateral epicondylitis (LE), also known as tennis elbow, affects 1–3% of the population, primarily those over 40 years old. Most cases resolve with conservative treatments, but some require more advanced interventions. Extracorporeal shockwave therapy (ESWT) has emerged as a non-surgical treatment option, utilizing either low- or high-energy levels to alleviate pain and improve function. Objective: This study aimed to compare the efficacy of low-energy versus high-energy ESWT in the treatment of chronic LE, focusing on pain relief and functional improvement. Methods: A retrospective observational study was conducted including patients treated for chronic LE between 2021 and 2024. Participants were divided into two groups: low-energy ESWT (0.10 mJ/mm²) and high-energy ESWT (0.20 mJ/mm²). Both groups received 2400 pulses at a frequency of 6 Hz once a week for three weeks. Pain and functional outcomes were measured using a visual analog scale (VAS) and the Patient-Rated Tennis Elbow Evaluation Questionnaire (PRTEE) at the baseline, three months (T1), and six months (T2) post-treatment. Results: Forty-six patients participated, with 24 in the low-energy group and 22 in the high-energy group. Baseline demographics and clinical characteristics were similar across groups. At T1 and T2, the low-energy group showed significantly greater reductions in the VAS scores (T1: 4.45 ± 0.8 vs. 3.6 ± 1.7, p = 0.04; T2: 3.2 ± 1.2 vs. 2.1 ± 1.1, p = 0.004) and PRTEE scores (T1: 34.3 ± 6.9 vs. 26.8 ± 11.9, p = 0.03; T2: 25.3 ± 6 vs. 17.6 ± 9, p = 0.005). Significant treatment–time interactions were observed for both the VAS and PRTEE scores, indicating sustained improvements in the low-energy group. Conclusions: Low-energy ESWT was more effective than high-energy ESWT in treating chronic LE, providing greater and longer-lasting pain relief and functional improvement. These findings suggest that low-energy ESWT should be preferred in clinical practice for managing this condition. Future research should focus on larger sample sizes and randomized controlled trials to confirm these results and explore the underlying mechanisms of differential efficacy between energy levels.

The supraspinatus tendon is one of the most involved tendons in the development of shoulder pain. Extracorporeal shockwave therapy (ESWT) has been recognized as a valid and safe treatment. Sometimes the symptoms cannot be relieved, or a relapse develops, affecting the patient’s quality of life. Therefore, a prediction protocol could be a powerful tool aiding our clinical decisions. An artificial neural network was run, in particular a multilayer perceptron model incorporating input information such as the VAS and Constant–Murley score, administered at T0 and at T1 after six months. It showed a model sensitivity of 80.7%, and the area under the ROC curve was 0.701, which demonstrates good discrimination. The aim of our study was to identify predictive factors for minimal clinically successful therapy (MCST), defined as a reduction of ≥40% in VAS score at T1 following ESWT for chronic non-calcific supraspinatus tendinopathy (SNCCT). From the male gender, we expect greater and more frequent clinical success. The more severe the patient’s initial condition, the greater the possibility that clinical success will decrease. The Constant and Murley score, Roles and Maudsley score, and VAS are not just evaluation tools to verify an improvement; they are also prognostic factors to be taken into consideration in the assessment of achieving clinical success. Due to the lower clinical improvement observed in older patients and those with worse clinical and functional scales, it would be preferable to also provide these patients with the possibility of combined treatments. The ANN predictive model is reasonable and accurate in studying the influence of prognostic factors and achieving clinical success in patients with chronic non-calcific tendinopathy of the supraspinatus treated with ESWT.

The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model’s effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3%, a maximum sensitivity of 93.3%, and a maximum specificity of 78.6% is reached. Finally, for general abnormalities, the explored models reach 83.7%, 90.0% and 84.2% of maximum accuracy, sensitivity and specificity, respectively.

Water-based rehabilitation is a well-known approach that is useful for the prevention and management of many conditions. Its application in the elderly is quite common in clinical practice, but scientific evidence is limited. We conducted a scoping review on geriatric water-based rehabilitation using a methodology borrowed from data science to investigate and discuss the extensive literature data. We searched the papers on PubMed and we used the abstracts to collect different data. We imported them into an electronic database and we used its filters to build different graphical representations. The filters allowed the selections of specific modalities of a variable and the following visualization of the values of the other variables linked to that selected modality. A total of 49 papers were found and they confirmed the safety and usefulness of water-based rehabilitation. Our analysis was able to show the relationships among the variables and the differences in the elements considered for the analysis. The current literature shows some limitations, especially concerning the article types in some specific diseases and the outcome measurements. Future research can overcome these limitations by collecting more data on the diseases that affect old people, even with the use of precise outcome measures. Our described methodology can be potentially beneficial and other studies may confirm its utility.