Categorical outcome analyses in randomized controlled trials (RCTs) and observational studies are commonly presented as relative risks (RRs) and odds ratios (ORs). In some situations, these RRs and ORs may be misunderstood, resulting in wrong conclusions. How this may happen is explained in the context of a hypothetical RCT that compares potentially lifesaving drugs A and B with placebo. In this RCT, the RR for survival is 1.67 for A vs placebo and 1.42 for B vs placebo. Using these RR data, as a challenge, readers are invited to answer 2 questions either intuitively or by other means. First, by how much is A better than B? Second, if the absolute survival rate with B is 8.5%, using the answer obtained from the previous question, what is the absolute survival rate with A? In this same RCT, the OR for survival is 1.74 for A vs placebo and 1.46 for B vs placebo. Using the OR data instead of the RR data, readers are again invited to answer the 2 questions listed above. This article explains why it is easy for readers and even authors to arrive at wrong answers to the 2 questions and draw wrong conclusions about the results. This article also explains what the correct answers are and how they may be obtained. The explanations involve simple concepts and even simpler arithmetic.
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http://dx.doi.org/10.4088/JCP.23f14943 | DOI Listing |
J Gen Intern Med
January 2025
Executive Division, National Center for PTSD, White River Junction, USA.
Background: Moral injury affects a variety of populations who make ethically complex decisions involving their own and others' well-being, including combat veterans, healthcare workers, and first responders. Yet little is known about occupational differences in the prevalence of morally injurious exposures and outcomes in nationally representative samples of such populations.
Objective: To examine prevalence of potentially morally injurious event (PMIE) exposure and clinically meaningful moral injury in three high-risk groups.
Metabolites
December 2024
Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, 0130 Oslo, Norway.
: Metabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g.
View Article and Find Full Text PDFJ Assist Reprod Genet
January 2025
Department of Obstetrics and Gynecology, Cairo University, Cairo, Egypt.
PGT-A, what's it for? Considering the increase in fetal aneuploidies with a woman's age and the high number of miscarriages associated with fetal karyotype anomalies, the concept of selecting IVF embryos based on their karyotype in order to transfer only euploid embryos and eliminate aneuploid ones was proposed. Preimplantation genetic testing for aneuploidy (PGT-A) was then established, nearly 30 years ago, with the expectation that the transfer of euploid embryos would lead to a significant improvement in medically assisted reproduction (MAR) outcomes. PGT-A, what's wrong? Despite the practice and widespread use, PGT-A has not consistently proven its effectiveness.
View Article and Find Full Text PDFEye (Lond)
January 2025
Ophthalmology Department, Norfolk & Norwich University Hospital, Norwich, UK.
Background: This study presents a comprehensive evaluation of the performance of various large language models in generating responses for ophthalmology emergencies and compares their accuracy with the established United Kingdom's National Health Service 111 online system.
Methods: We included 21 ophthalmology-related emergency scenario questions from the NHS 111 triaging algorithm. These questions were based on four different ophthalmology emergency themes as laid out in the NHS 111 algorithm.
Urogynecology (Phila)
January 2025
From the Division of Urogynecology, Walter Reed National Military Medical Center, Bethesda, MD.
Importance: Use of the publicly available Large Language Model, Chat Generative Pre-trained Transformer (ChatGPT 3.5; OpenAI, 2022), is growing in health care despite varying accuracies.
Objective: The aim of this study was to assess the accuracy and readability of ChatGPT's responses to questions encompassing surgical informed consent in urogynecology.
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