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Preferred labels and language to improve communication about lesions at low risk of progressing to cancer: qualitative interviews with patients and physicians.
BMJ Open
January 2025
Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada
Objectives: We explored how to improve communication about low-risk lesions including labels, language and other strategies.
Design: Qualitative description and thematic analysis to examine the transcripts of telephone interviews with patients who had low-risk lesions and physicians; and mapping to Communication Accommodation Theory to interpret themes.
Setting: Canada PARTICIPANTS: 15 patients: 6 (40%) bladder, 5 (33%) prostate and 4 (27%) cervix lesions; and 13 physicians: 7 (54%) cervix, 3 (23%) bladder and 3 (23%) prostate lesions.
Patellar Instability After Total Knee Arthroplasty.
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Lenox Hill Hospital Department of Orthopedic Surgery, Northwell Health, New York, United States.
Patellar instability following total knee arthroplasty (TKA) is a rare, yet serious complication, potentially requiring revision surgery or resulting in chronic dysfunction. When encountered, it is paramount to understand the etiologies, diagnostic approaches, treatment options, and outcomes of the selected treatment. The most common cause of patella instability is improper positioning of components, leading to lateral maltracking of the patella.
View Article and Find Full Text PDFEmpowering parents with intellectual disabilities: key supports and challenges in Canada's evolving child protection system.
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Rethinking clinical guidance: the imperative for evidence-based and inclusive practices in managing gender dysphoria in youth.
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Major Adverse Kidney Events in Hospitalized Older Patients With Acute Kidney Injury: Machine Learning-Based Model Development and Validation Study.
J Med Internet Res
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Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.
Background: Acute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI.
Objective: This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.
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