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725 results for “stroke rehabilitation”
The State of Stroke Rehabilitation Design in Australia: A multi-scalar Systematic Architecture Precedent Review
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Dataset related to article "Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy"
<p>DATASET #1</p> <p>Il data set è composto da 174 osservazioni riferite ad un campione di n=174 pazienti.</p> <p>Le variabili prese in considerazione per lo studio del data set sono 21:</p> <ul> <li> <p>ID_Pazient: variabile quantitativa continua, indica il numero di identificazione del paziente</p> </li> <li> <p>Sex: variabile dicotomica, indica il sesso del paziente (Maschio=0, Femmina=1)</p> </li> <li> <p>Age: variabile quantitativa continua, indica l'età del paziente nel momento in cui è stata effettuata la valutazione</p> </li> <li> <p>EMG_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del soggetto di controllare il dispositivo con i propri segnali elettromiografici</p> </li> <li> <p>Force_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del paziente di controllare il dispositivo con la propria forza</p> </li> <li> <p>Month_Injury: variabile quantitativa continua, indica i mesi trascorsi dalla data in cui è avvenuto l'ictus</p> </li> <li> <p>Diagnosis: variabile dicotomica, indica la tipologia di ictus: (Ischemico=0, Emorragico =1)</p> </li> <li> <p>Hemisphere: variabile dicotomica, indica quale emisfero cerebrale è stato colpito dall'ictus (Destro=0, Sinistro=1)</p> </li> <li> <p>FM_UE: variabile quantitativa discreta, indica la misura della funzione motoria dell'arto superiore determinata somministrando la scala Fugl-Meyer Upper Extremity</p> </li> <li> <p>Sensitivity: variabile quantitativa discreta, indica la sezione per la misura della sensibilità della scala Fugl-Meyer</p> </li> <li> <p>Pain_ROM: variabile quantitativa discreta, indica la sezione per la misura di articolarità e dolore della scala Fugl-Meyer</p> </li> <li> <p>FIM: variabile quantitativa discreta, indica la misura di autonomia della persona nelle attività della vita quotidiana, determinata dalla somministrazione della scala Functional Independence Measure</p> </li> <li> <p>RPS: variabile quantitativa discreta, indica la misura della funzione di raggiungimento di un oggetto</p> </li> <li> <p>Peg_Sec: variabile quantitativa continua, indica la misura della destrezza manuale fine e coincide con il rapporto tra il numero di pioli e i secondi impiegati per inserirli in uno specifico supporto</p> </li> <li> <p>PectMaj: variabile qualitativa ordinata, indica la misura della spasticità del pettorale, secondo la Modified Ashworth Scale</p> </li> <li> <p>BicBrach: variabile qualitativa ordinata, indica la misura della spasticità del bicipite, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexCarp: variabile qualitativa ordinata, indica la misura della spasticità del flessore del carpo, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexProfDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore profondo delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexSupDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore superficiale delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>Ashworth_TOT: variabile quantitativa discreta, indica la misura totale della Modified Ashworth Scale, data dalla somma delle 5 variabili precedenti</p> </li> <li> <p>BB_par: variabile quantitativa discreta, indica la misura della destrezza manuale grossolana dell'arto paretico</p> </li> </ul>
Dataset related to article: "Association Between Malnutrition and Outcomes in Patients With Severe Ischemic Stroke Undergoing Rehabilitation"
<p>We provide the raw data used for the following article:</p> <ul> <li>Scrutinio D, Lanzillo B, Guida P, Passantino A, Spaccavento S, Battista P.<br> <em>Association Between Malnutrition and Outcomes in Patients With Severe Ischemic Stroke Undergoing Rehabilitation</em><br> "Arch Phys Med Rehabil." 2020 May;101(5):852-860.<br> doi: 10.1016/j.apmr.2019.11.012. Epub 2019 Dec 28. PMID: 31891712.</li> </ul> <p> </p> <p><strong>Abstract</strong><br> <strong>Objective: </strong>To investigate the incremental prognostic significance of malnutrition in patients with severe poststroke disability.<br> <strong>Design: </strong>Retrospective cohort study. The patients were recruited from 3 specialized inpatient rehabilitation facilities. Nutritional status was assessed using the Prognostic Nutritional Index (PNI), which is calculated from serum albumin and total lymphocyte count. Scores >38 points reflect normal nutrition status, scores of 35-38 indicate moderate malnutrition, and scores <35 indicate severe malnutrition. The association of PNI categories with outcomes was assessed using multivariable regression analyses.<br> <strong>Setting: </strong>Inpatient rehabilitation facility.<br> <strong>Participants:</strong> Patients (NZ668) with ischemic stroke admitted to inpatient rehabilitation within 90 days from stroke occurrence and classified as Case-Mix Groups 0108, 0109, and 0110 of the current Medicare case-mix classification system.<br> <strong>Interventions: </strong>Not applicable.<br> <strong>Main Outcome Measures:</strong> Three outcomes were examined: (1) the combined outcome of transfer to acute care and death within 90 days from admission to rehabilitation; (2) 2-year mortality; and (3) FIM motor effectiveness, calculated as (FIM motor change/maximum FIM motoradmission FIM motor score)100.<br> <strong>Results: </strong>Overall, the median time to rehabilitation admission was 18 days (range, 12-26 days). The prevalence of moderate and severe malnutrition was 12.7% and 11.5%, respectively. Ninety-one patients (13.6%) experienced the combined outcome. After adjusting for independent predictors including sex, atrial fibrillation, dysphagia, FIM cognitive score, and hemoglobin levels, neither moderate (PZ.280) nor severe malnutrition (PZ.482) were associated with the combined outcome. Similar results were observed when looking at 2-year mortality. Overall, FIM motor effectiveness was 30%24%. After adjusting for independent predictors, severe malnutrition (b coefficient 0.4580.216; PZ.034) was associated with FIM motor effectiveness.<br> <strong>Conclusions:</strong> Approximately 1 in every 9 patients presented severe malnutrition. On top of the independent predictors, severe malnutrition did not provide additional prognostic information concerning risk of the combined outcome or 2-year mortality. Conversely, severe malnutrition was associated with poorer functional outcome as expressed by FIM motor effectiveness.</p>
Rehabilitation of post-stroke aphasia by a single protocol targeting phonological, lexical, and semantic deficits with speech output tasks
<p>This study assessed the effectiveness of a novel rehabilitation protocol (PHOLEXSEM), focused on PHonological, SEmantic, and LExical deficits, aiming at improving lexical retrieval, and, generally, spoken output. The study is published here: https://doi.org/10.23736/S1973-9087.24.08576-9</p> <p><span>These data cannot be made publicly available because they include sensitive information, but can be made available to interested researchers upon reasonable request. Please, forward your request to Prof. Nadia Bolognini (n.bolognini@auxologico.it).</span></p>
Dataset related to article: "Machine learning to predict mortality after rehabilitation among patients with severe stroke"
<p>We provide the raw data used for the following article:</p> <ul> <li>Scrutinio D, Ricciardi C, Donisi L, Losavio E, Battista P, Guida P, Cesarelli M, Pagano G, D'Addio G.<br> <em>Machine learning to predict mortality after rehabilitation among patients with severe stroke.</em> "Sci Rep." 2020 Nov 18;10(1):20127.<br> doi: 10.1038/s41598-020-77243-3. PMID: 33208913; PMCID: PMC7674405.</li> </ul> <p> </p> <p><strong>Abstract:</strong> Stroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.