Extracorporeal shock waves are high-intensity mechanical waves (500–1000 bar) of a microsecond duration with a morphology characterized by a rapid positive phase followed by a negative phase. Background: Extracorporeal shock waves have been used for pain treatment for various sub-acute and chronic musculoskeletal (MSK) problems since 2000. The aim of this article is to update information on the role of extracorporeal shock wave therapy (ESWT) in the treatment of various pathologies that cause MSK pain. Methods: Given that in the last two years, articles of interest (including systematic reviews and meta-analyses) have been published on less known indications, such as low back pain, nerve entrapments, osteoarthritis and bone vascular diseases, a literature search was conducted in PubMed, the Cochrane Database, EMBASE, CINAHL and PEDro, with the aim of developing a narrative review of the current literature on this topic. The purposes of the review were to review possible new mechanisms of action, update the level of evidence for known indications and assess possible new indications that have emerged in recent years. Results: Although extracorporeal shock waves have mechanical effects, their main mechanism of action is biological, through a phenomenon called mechanotransduction. There is solid evidence that supports their use to improve pain in many MSK pathologies, such as different tendinopathies (epicondylar, trochanteric, patellar, Achilles or calcific shoulder), plantar fasciitis, axial pain (myofascial, lumbar or coccygodynia), osteoarthritis and bone lesions (delayed union, osteonecrosis of the femoral head, Kienbock’s disease, bone marrow edema syndrome of the hip, pubis osteitis or carpal tunnel syndrome). Of the clinical indications mentioned in this review, five have a level of evidence of 1+, eight have a level of evidence of 1−, one indication has a level of evidence of 2− and two indications have a level of evidence of 3. Conclusions: The current literature shows that ESWT is a safe treatment, with hardly any adverse effects reported. Furthermore, it can be used alone or in conjunction with other physical therapies such as eccentric strengthening exercises or static stretching, which can enhance its therapeutic effect.

The three horizons model is a framework that helps manage an organization’s innovation strategy. This model considers three aspects (horizons) that should be present in the institution and guide the development of new systems. Applied to medical science, the horizons are considered as paradigms that set the guidelines for clinical knowledge. New technologies can influence this model by causing disruptive changes. Horizon 1 (evidence-based medicine) reflects the current paradigm and emphasizes the aspect of continuous improvement needed to strengthen it, such as with the introduction of the GRADE (Grades of Recommendation Assessment, Development, and Evaluation) methodology. Evidence-based medicine has made it possible to stop performing harmful interventions like autologous bone marrow or stem cell transplantation in cancer treatment for women with early poor prognosis breast cancer or to discontinue the erroneous belief that children should not sleep on their backs to prevent sudden infant death syndrome. Horizon 2 (real-world evidence) refers to a new model in which innovation has generated new capabilities. This change makes it possible to correct weaknesses of the previous paradigm, as in the case of pragmatic clinical trials. Real-world evidence has been used to show that drugs such as tofacitinib are effective without using methotrexate as background or to demonstrate the efficacy of chemotherapy in older patients with stage II colon cancer. Horizon 3 (precision medicine) involves a disruptive innovation, leading to the abandonment of the traditional mechanistic model of medical science and is made possible by the appearance of major advances such as artificial intelligence. Precision medicine has been used to assess the use of retigabine for the treatment of refractory epilepsy or to define a genome-adjusted radiation dose using a biological model to simulate the response to radiotherapy, facilitate dose adjustment and predict outcome in breast cancer.

Functional decline in older adults represents a major challenge in geriatric rehabilitation, and machine learning (ML) clustering techniques may help identify functional recovery profiles and support personalized rehabilitation strategies. This study characterizes functional recovery profiles in older adults admitted for rehabilitation using unsupervised clustering techniques. A retrospective longitudinal study was conducted including 957 older adults admitted to a geriatric rehabilitation unit between 2019 and 2025. Clinical and functional variables, including the Modified Barthel Index (MBI), Daniels and Worthingham’s Muscle Testing, and Functional Ambulation Category, were collected from medical records. Considering the baseline functional status and the temporal changes in MBI scores, a clustering of the functional trajectories has been performed using a k-means algorithm based on the silhouette score. The robustness of the resulting segmentation has been evaluated by comparing alternative partitions obtained from bootstrap-sampling, hierarchical clustering, and the centroids of Gaussian Mixture Models. Four distinct functional recovery profiles were identified, showing different trajectories of independence, ambulation, and muscle strength during rehabilitation. Two clusters demonstrated favorable recovery and higher functional resilience, whereas the remaining profiles were characterized by chronic impairment or severe damage with limited recovery. These findings support the usefulness of unsupervised ML clustering techniques for identifying clinically meaningful recovery profiles and may facilitate patient stratification and individualized intervention planning in geriatric rehabilitation.