Study Objective: To determine the practice of members of the Society of Ambulatory Anesthesia (SAMBA) in the management of postoperative nausea and vomiting (PONV) before and after the Food and Drug Administration (FDA) black box warning on droperidol.
Design: Survey questionnaire.
Setting: The Society of Ambulatory Anesthesia.
Measurements: After institutional review board approval, a survey was posted on the SAMBA Web site from June 1, 2005, until October 30, 2005. Visitors of the Web site were invited to participate in the survey. The survey was designed to elicit information about the management of PONV, particularly the use of droperidol, before and after the FDA black box warning. Participants were also asked about reasons for not using droperidol in their current practice and whether they believed that the black box warning was justified.
Main Results: Two hundred ninety-five physicians of 1,179 eligible SAMBA members completed the survey for a 25% response rate. For PONV prophylaxis, the choice of droperidol as a first-line agent decreased from 47% to 5% after the black box warning appeared (P < 0.0001). Similarly, for treatment of established PONV, the choice of droperidol decreased from 38% to 8% during this same period (P < 0.0001). A total of 261 (92%) of responders did not believe that the black box warning was justified.
Conclusions: Although most surveyed practitioners believed that the FDA black box warning on droperidol is not justified, the use of this cost-effective agent has significantly declined.
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http://dx.doi.org/10.1016/j.jclinane.2007.08.003 | DOI Listing |
Environ Int
January 2025
School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah 21589, Saudi Arabia. Electronic address:
Black carbon is harmful for climate, environment, and human health. Road traffic is one of the major sources for black carbon in urban areas. This study develops a street scale air quality model configuration for the dispersion of black carbon concentrations across the West Midlands, UK, incorporating updated road traffic emission factors.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability.
View Article and Find Full Text PDFAnn Rheum Dis
January 2025
Department of Surgery, University of Cambridge, Cambridge, UK.
Objectives: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period.
Methods: We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative-the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation-we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary.
J Am Med Inform Assoc
January 2025
Department of Computer Science, Duke University, Durham, NC 27708, United States.
Objective: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.
Material And Methods: We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them.
Nicotine Tob Res
January 2025
Behavioral Health and Health Policy, Westat, 1600 Research Blvd, Rockville, MD 20850, United States.
Introduction: Pregnant people who smoke constitute a uniquely vulnerable population likely to be impacted by a menthol cigarette (MC) ban. However, there are no published reports of prevalence of prenatal MC use in a nationally-representative US sample including racial-ethnic disparities and associated characteristics.
Methods: Participants were 1245 US pregnant people who smoked MC or non-MC (NMC) in the past 30-days from the 2010-2019 National Survey on Drug Use and Health.
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