Objective: The purpose of this study was to determine the clinical efficacy of manual therapy interventions for relieving the signs and symptoms of carpal tunnel syndrome (CTS) by comparing 2 forms of manual therapy techniques: Graston Instrument-assisted soft tissue mobilization (GISTM) and STM administered with the clinician hands.
Methods: The study was a prospective comparative research design in the setting of a research laboratory. Volunteers were recruited with symptoms suggestive of CTS based upon a phone interview and confirmed by electrodiagnostic study findings, symptom characteristics, and physical examination findings during an initial screening visit. Eligible patients with CTS were randomly allocated to receive either GISTM or STM. Interventions were, on average, twice a week for 4 weeks and once a week for 2 additional weeks. Outcome measures included (1) sensory and motor nerve conduction evaluations of the median nerve; (2) subjective pain evaluations of the hand using visual analog scales and Katz hand diagrams; (3) self-reported ratings of symptom severity and functional status; and (4) clinical assessments of sensory and motor functions of the hand via physical examination procedures. Parametric and nonparametric statistics compared treated CTS hand and control hand and between the treatment interventions, across time (baseline, immediate post, and at 3 months' follow-up).
Results: After both manual therapy interventions, there were improvements to nerve conduction latencies, wrist strength, and wrist motion. The improvements detected by our subjective evaluations of the signs and symptoms of CTS and patient satisfaction with the treatment outcomes provided additional evidence for the clinical efficacy of these 2 manual therapies for CTS. The improvements were maintained at 3 months for both treatment interventions. Data from the control hand did not change across measurement time points.
Conclusions: Although the clinical improvements were not different between the 2 manual therapy techniques, which were compared prospectively, the data substantiated the clinical efficacy of conservative treatment options for mild to moderate CTS.
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http://dx.doi.org/10.1016/j.jmpt.2006.11.014 | DOI Listing |
Cancer Imaging
January 2025
Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Background: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.
View Article and Find Full Text PDFComplement Med Res
January 2025
Background: Cupping therapy, a traditional treatment method, has been shown to be effective in various studies. However, there have been reports of significant neurological complications following cupping therapy. This comprehensive review aimed to investigate the important and potentially severe neurological complications documented in the literature.
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January 2025
Doctora en Psicología Clínica (DClinPsy). Catedrática de Psicología Clínica de la Vejez, Colegio Universitario de Londres. Directora del Centro Internacional de Terapia de Estimulación Cognitiva. .
Introducción: La Terapia de Estimulación Cognitiva (TEC) se originó en el Reino Unido. Se trata de un programa de estimulación que ha demostrado en un estudio multicéntrico, leatorizado y de diseño controlado beneficios en la cognición y la calidad de vida de los pacientes con demencia. La TEC fue adaptada e implementada en más de 30 países.
View Article and Find Full Text PDFRadiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
View Article and Find Full Text PDFFront Neurol
January 2025
Department of Oral Medical Science and Biotechnology, Physical and Rehabilitation Medicine, BIND, CARES, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy.
Introduction: Cerebral palsy (CP) is a group of permanent disorders of movement development that may cause activity limitations. In this context, robot-assisted therapy might play a key role in clinical management. This comprehensive systematic review aimed to investigate the efficacy of robotic systems in improving upper limb (UL) functions in children with CP.
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