Objective: To compare the quality of robotic prostatectomy surgical videos on the popular website YouTube with more curated, professional sources using the Global Evaluative Assessment of Robotic Skills (GEARS) criteria.
Methods: A search was performed on YouTube for robotic prostatectomy. Results were sorted by views and the first ten that met inclusion criteria were selected for review. To represent curated sources five robotic prostatectomy videos were selected from the DaVinci Surgery Community (DVS) video repository and the AUA Surgical Video Library in order of publishing from present to past. Videos were edited to be deidentified. The videos were reviewed blindly in parallel and graded using the GEARS criteria. Concordance among reviewers was measured using Chronbach's alpha. Comparisons between groups were made using student t-test.
Results: There was a high level of reliability of overall GEARS scores between reviewers for each video (α = 0.843). There was no significant difference between overall GEARS scores between the YouTube videos (mean 24.8, SDEV 1.85) and the AUA group (mean 24.3, SDEV 6.18) (P = 0.78). YouTube videos scored higher than the DVS videos (mean 22.1, SDEV 2.34) (P 0.03).
Conclusion: Despite concerns about the quality of surgical videos on YouTube for education, the most viewed surgical videos for robot assisted laparoscopic prostatectomy score as well or better than more curated sources using the GEARS criteria. This may represent selection via crowd sourcing of the best videos amongst a much larger overall quantity.
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http://dx.doi.org/10.1016/j.urology.2021.04.005 | DOI Listing |
Stat Med
February 2025
Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
With the increasing maturity of genetic profiling, an essential and routine task in cancer research is to model disease outcomes/phenotypes using genetic variables. Many methods have been successfully developed. However, oftentimes, empirical performance is unsatisfactory because of a "lack of information.
View Article and Find Full Text PDFData Brief
February 2025
SCES, Strathmore University, Nairobi, Kenya.
Quantum Cascade Lasers (QCL) are promising semiconductor lasers, compact and powerful, but of complex design. Availability of structured data of the QCL properties can support data mining activities that seek to understand the relationship between these properties, for instance between the design and performance features. The main open source of QCL data is in scientific text which in most cases is usually unstructured.
View Article and Find Full Text PDFJ Invest Dermatol
January 2025
Department of Dermatology, Medical University of Vienna, Vienna, Austria.
Learn Health Syst
January 2025
Department of Biomedical Informatics University of Arkansas for Medical Sciences, College of Medicine Little Rock Arkansas USA.
Objective: This project demonstrates the feasibility of connecting medical imaging data and features, SARS-CoV-2 genome variants, with clinical data in the National Clinical Cohort Collaborative (N3C) repository to accelerate integrative research on detection, diagnosis, and treatment of COVID-19-related morbidities. The N3C curated a rich collection of aggregated and de-identified electronic health records (EHR) data of over 18 million patients, including 7.5 million COVID-positive patients, seen at hospitals across the United States.
View Article and Find Full Text PDFBiol Rev Camb Philos Soc
January 2025
Wildlife Observatory of Australia (WildObs), Queensland Cyber Infrastructure Foundation (QCIF), Brisbane, Queensland, 4072, Australia.
Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, and potential for increasing impact. We investigated a decade of use of wildlife cameras (2012-2022) with a case study on Australian terrestrial vertebrates using a multifaceted approach. We (i) synthesised information from a literature review; (ii) conducted an online questionnaire of 132 professionals; (iii) hosted an in-person workshop of 28 leading experts representing academia, non-governmental organisations (NGOs), and government; and (iv) mapped camera trap usage based on all sources.
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