Soft Tissue Sarcoma with Lower Limb Impairment: Development of a Specific Rehabilitation Protocol Based on Demolitive and Reconstructive Surgery Types.

Soft tissue sarcomas (STSs) are extremely uncommon tumors with a high rate of local recurrence that often require very demolitive surgery. The aim of our study is to propose a specific rehabilitation protocol for patients with STSs, based on the kind of demolition and reconstructive surgery performed, and evaluate its effects. : The protocol was developed on the basis of the clinical experiences of physiatrists and surgeons, as well as data from the literature, recommending timelines for postural steps, verticalization, walking, and therapeutic exercises, in accordance with wound healing times and in order to prevent complications from disuse and immobility.

Premorbid frailty, stress hyperglycemia ratio, and functional outcome in patients with acute ischemic stroke.

Background: Frailty, defined as multidimensional prognostic index (MPI), has been recently identified as strong predictor of disability and mortality in the elderly with acute ischemic stroke (AIS). The stress hyperglycemia ratio (SHR) is a recently introduced biomarker significantly associated with poor outcome in AIS.

Objectives: This study aimed to investigate in what extent frailty, measured by MPI, and SHR affects the 3-months outcome of patients > 65 years-old with AIS.

Effectiveness of Two Models of Telerehabilitation in Improving Recovery from Subacute Upper Limb Disability after Stroke: Robotic vs. Non-Robotic.

Background/objectives: Finding innovative digital solutions is fundamental to ensure prompt and continuous care for patients with chronic neurological disorders, whose demand for rehabilitation also in home-based settings is steadily increasing. The aim is to verify the safety and the effectiveness of two telerehabilitation (TR) models in improving recovery from subacute upper limb (UL) disability after stroke, with and without a robotic device.

Methods: One hundred nineteen subjects with subacute post-stroke UL disability were assessed for eligibility.

Progression to refractory status epilepticus: A machine learning analysis by means of classification and regression tree analysis.

Background And Objectives: to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique.

Methods: Consecutive patients aged ≥ 14 years with SE registered in a 9-years period at Modena Academic Hospital were included in the analysis. We evaluated the risk of progression to RSE using logistic regression and a machine learning analysis by means of classification and regression tree analysis (CART) to develop a predictive model of progression to RSE.