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Musculoskeletal Computed Tomography: How to Add Value When Reporting Adult Lower Limb Trauma.
J Comput Assist Tomogr
November 2024
Department of Trauma and Orthopaedics, University Hospitals of Morecambe Bay NHS Trust, Lancaster, United Kingdom.
Computed tomography plays an ever-increasing role in the management of fractures and dislocations due to its capability in efficiently providing multiplanar reformats and 3-dimensional volume rendered images. It can reveal findings that are occult on plain radiography and therefore allow for more accurate decision making with regard to fracture classification and management. Clinical radiologists play a critical role in facilitating the processing of imaging to provide adequate image reformats in the desired planes, producing 3 dimensional images but most crucially identifying pertinent findings, which will contribute between the selection of nonoperative and operative management and potentially influence surgical technique.
View Article and Find Full Text PDFProstate Cancer Risk Prediction Model Using Clinical and Magnetic Resonance Imaging-Related Findings: Impact of Combining Lesions' Locations and Apparent Diffusion Coefficient Values.
J Comput Assist Tomogr
November 2024
From the Department of Radiology, Mayo Clinic, Rochester, MN.
Objectives: The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)-related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance.
Methods: This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa.
Use Of Patient Health Survey Data For Risk Adjustment To Limit Distortionary Coding Incentives In Medicare.
Health Aff (Millwood)
January 2025
Michael E. Chernew, Harvard University.
A core problem with the current risk-adjustment system in Medicare Advantage and accountable care organization (ACO) programs-the Hierarchical Condition Categories (HCC) model-is that the inputs (coded diagnoses) can be influenced for gain by risk-bearing plans or providers. Using existing survey data on health status (which provide less manipulable inputs), we found that the use of a hybrid risk score drawing from survey data and a scaled-back set of HCCs would, in addition to mitigating coding incentives, modestly lessen risk-selection incentives, strengthen payment incentives to deliver efficient care, allocate payment across ACOs more efficiently according to markers of population health that are not as affected by practice patterns or coding efforts, and redistribute payment in a manner that supports equity goals. Although sampling error and survey nonresponse present challenges, analyses suggest that these should not be prohibitive.
View Article and Find Full Text PDF"Choosing wisely": Intravitreal gas injection as first-line treatment for macular hole-a clinical case perspective.
Retin Cases Brief Rep
January 2025
Chair of Ophthalmology division; Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv, Israel.
Purpose: To evaluate the potency and security of Pneumatic Vitreolysis (PVL) as the primary treatment for Full-Thickness Macular Holes (FTMHs) and provide insights into patient selection criteria and procedural outcomes.
Patients And Methods: A retrospective analysis of three clinical cases presenting with FTMHs treated initially with PVL was conducted. Cases were evaluated for anatomical and functional outcomes through comprehensive ophthalmic examination and optical coherence tomography (OCT) imaging.
Ensemble learning-based predictor for driver synonymous mutation with sequence representation.
PLoS Comput Biol
January 2025
School of Biomedical Engineering, Anhui Medical University, Hefei, China.
Synonymous mutations, once considered neutral, are now understood to have significant implications for a variety of diseases, particularly cancer. It is indispensable to identify these driver synonymous mutations in human cancers, yet current methods are constrained by data limitations. In this study, we initially investigate the impact of sequence-based features, including DNA shape, physicochemical properties and one-hot encoding of nucleotides, and deep learning-derived features from pre-trained chemical molecule language models based on BERT.
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