Background: Although several rehabilitation interventions are effective in post-stroke aphasia (PSA), the efficacy of different rehabilitation interventions compared to each other remains controversial. Here, we aimed to compare the effectiveness of varying rehabilitation interventions in PSA.
Methods: Randomized controlled trials on 8 kinds of rehabilitation interventions to improve speech function in patients with PSA were searched by computer from 10 databases, including PubMed, Web of Science, Cochrane, OVID, CINAHL, Embase, CNKI, WanFang, CBM, and VIP. The search scope was from the establishment of the database to August 2023. The literature screening, extraction of basic information, and quality assessment of the literature were conducted independently by 2 researchers. Network meta-analysis (NMA) was performed using Stata 17.0 software.
Results: Fifty-four studies involving 2688 patients with PSA were included. The results of NMA showed that: ① in terms of improving the severity of aphasia, the therapeutic effects of repetitive transcranial magnetic stimulation were the most significant; ② motor imagery therapy was the most effective in improving spontaneous speech, repetition, and naming ability; ③ in terms of improving listening comprehension ability, the therapeutic effects of mirror neuron therapy was the most significant.
Conclusion: The 8 rehabilitation interventions have different focuses in improving the speech function of PSA patients, and the clinical therapists can select the optimal rehabilitation interventions in a targeted manner according to the results of this NMA and the patients' conditions and other relevant factors.
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http://dx.doi.org/10.1097/MD.0000000000038255 | DOI Listing |
J Med Internet Res
January 2025
Knight Foundation of Computing & Information Sciences, Florida International University, Miami, FL, United States.
Background: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Background: Acquired neurological diseases entail significant changes and influence the relationship between a patient and their significant other. In the context of long-term rehabilitation, those affected collaborate with health care professionals who are expected to have a positive impact on the lives of the affected individuals.
Objective: This study aims to examine the changes in the relationship between the patient and their loved ones due to acquired neurological disorders and the influence of health care professionals on this relationship.
JAMA Intern Med
January 2025
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Importance: The optimal antiviral drug for treatment of nonsevere influenza remains unclear.
Objective: To compare effects of antiviral drugs for treating nonsevere influenza.
Data Sources: MEDLINE, Embase, CENTRAL, CINAHL, Global Health, Epistemonikos, and ClinicalTrials.
Anesth Analg
February 2025
SC Terapia Intensiva Neurochirurgica, Ospedale San Carlo Borromeo, ASST Santi Paolo e Carlo, Milano, Italy.
Background: Computed tomography (CT)-derived low muscle mass is associated with adverse outcomes in critically ill patients. Muscle ultrasound is a promising strategy for quantitating muscle mass. We evaluated the association between baseline ultrasound rectus femoris cross-sectional area (RF-CSA) and intensive care unit (ICU) mortality.
View Article and Find Full Text PDFInsights Imaging
January 2025
Medical Research Department, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, P. R. China.
Objective: To develop an automatic segmentation model to delineate the adnexal masses and construct a machine learning model to differentiate between low malignant risk and intermediate-high malignant risk of adnexal masses based on ovarian-adnexal reporting and data system (O-RADS).
Methods: A total of 663 ultrasound images of adnexal mass were collected and divided into two sets according to experienced radiologists: a low malignant risk set (n = 446) and an intermediate-high malignant risk set (n = 217). Deep learning segmentation models were trained and selected to automatically segment adnexal masses.
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