Objectives: To review the methodology and reporting of sample size calculations in a contemporary sample of trials in osteoarthritis.
Study Design And Setting: Randomized trials in hip and/or knee osteoarthritis published in 2016 were identified by searching MEDLINE, Cochrane library, CINAHL, EMBASE, PsycINFO, PEDro, and AMED until March 31, 2017. Data were extracted on study characteristics, methods used to calculate the sample size, and the reporting and justification of components used in the sample size calculation. We attempted to replicate the sample size calculation using the reported information.
Results: This review included 116 trials. Seventy-eight (67%, n = 78/116) reported a power calculation. Less than a quarter reported all core components of the sample size calculation (21%, n = 16/78). The sample size calculation was only reproducible in 53% of the trials that reported a power calculation (n = 41/78). The replicated calculation produced a sample size over 10% larger than the reported value in 12% of trials (n = 9/78). Insufficient information was reported to allow the sample size calculation to be replicated in a quarter of trials (27%, n = 21/78).
Conclusion: Sample size calculations in trials of hip and knee osteoarthritis are not adequately reported, and the calculation frequently cannot be reproduced.
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http://dx.doi.org/10.1016/j.jclinepi.2018.08.013 | DOI Listing |
Introduction: Glaucoma is a leading cause of blindness, often progressing asymptomatically until significant vision loss occurs. Early detection is crucial for preventing irreversible damage. The pupillary light reflex (PLR) has proven useful in glaucoma diagnosis, and mobile technologies like the AI-based smartphone pupillometer (AI Pupillometer) offer a promising solution for accessible screening.
View Article and Find Full Text PDFIndian Dermatol Online J
December 2024
Department of Dermatology, Venereology, and Leprosy, GSL Medical College and General Hospital, Rajahmahendravaram, Andhra Pradesh, India.
Background: Chronic spontaneous urticaria (CSU) appears to share some pathomechanisms with metabolic syndrome (MS), such as proinflammatory state, increased oxidative stress, changes in adipokine profile, and coagulation system activation.
Aim And Objectives: To evaluate clinical and laboratory parameters of MS in CSU patients and to assess relationship of MS with duration and severity of CSU, Ig-E, thyroid-stimulating hormone (TSH), C-reactive protein (CRP), and autologous serum skin test (ASST).
Materials And Methods: A hospital-based cross-sectional study was conducted on 131 CSU cases and 131 controls who were age- and sex-matched.
Indian Dermatol Online J
November 2024
Department of Dermatology, Venereology, and Leprosy, Gandhi Medical College, Secundarabad, Telangana, India.
Background: Diaper dermatoses broadly refer to skin disorders that occur in the diaper area. Dermoscopy is a non-invasive diagnostic tool that magnifies subsurface structures of the skin that are invisible to the unaided eye.
Aim: To identify and describe the dermoscopic features of dermatoses in the diaper area.
Indian Dermatol Online J
December 2024
The Venkat Centre for Advanced Dermatology, Plastic Surgery and ENT, Bengaluru, Karnataka, India.
Background: Trichoscopy is a simple, noninvasive tool to help in the diagnosis of various hair and scalp disorders. There is paucity of data on the normal trichoscopic parameters of hair density and diameter in the Indian population.
Aim: The aim of this study was to establish the trichoscopic patterns of hair and scalp in healthy Indian males and to provide a framework for future reference.
Comput Struct Biotechnol J
December 2024
Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.
Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis.
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