Objective: The aim of this study was to compare the cost of comprehensive outpatient therapy (day rehabilitation) in individuals with malignant brain tumors to those with stroke and traumatic brain injury.
Design: This was a prospective, nonrandomized, longitudinal study of 49 consecutive adults with malignant brain tumors enrolled in the 6 day rehabilitation sites of 1 institution over 35 months. The control group was composed of 50 patients with brain injury and 50 patients with stroke, who were also enrolled in the day rehabilitation program during the same period. A comparison was made of the total Medicare cost and the cost per day of day rehabilitation in patients with malignant brain tumors compared with the control group.
Results: The patients with malignant brain tumors had lower total cost and cost per day than did the combined traumatic brain injury and stroke group during day rehabilitation (F2,143 = 3.056 [P = 0.05] and F2,142 = 5.046 [P = 0.008], respectively).
Conclusions: The cost of comprehensive outpatient rehabilitation in patients with malignant brain tumors is less expensive than that of patients with traumatic brain injury or stroke, which are neurological diagnoses commonly seen in day rehabilitation. This study shows that cost should not be a barrier to providing outpatient therapies to this patient population.
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http://dx.doi.org/10.1097/PHM.0000000000000624 | DOI Listing |
Radiat Oncol
January 2025
Department of Radiotherapy and Radiooncology, Medical Faculty, Heinrich Heine University, Moorenstr. 5, 40225, Dusseldorf, Germany.
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Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Key Laboratory of Drug Research for Prevention and Treatment of Hyperlipidemic Diseases, Soochow University, 199 Ren'ai Road, Suzhou, 215123, Jiangsu, China.
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Dept. of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.
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View Article and Find Full Text PDFSci Rep
January 2025
Center for Informatics Science (CIS), School of Information Technology and Computer Science, Nile University, 26th of July Corridor, Sheikh Zayed City, Giza, 12588, Egypt.
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns.
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