Chest
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Retraction Of Publication
Clinical and Genetic Spectrum of Children with Primary Ciliary Dyskinesia in China.
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Mortality has long been used as a primary end point for randomized controlled trials in critical care. Recently, a plurality of trials targeting mortality end points as their primary outcome has failed to detect a difference between study arms. ⋯ We explore some of the reasons why such trials may be biased toward a neutral result, as well as reasons to consider alternative end points that are better coupled to the expected therapeutic effect. We also discuss to what extent mortality as a binary outcome is patient-important in the ICU.
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Providing guideline-concordant management of pulmonary nodules can present challenges when a patient's anxiety about cancer or fear of invasive procedures colors judgment. The way in which providers discuss and make decisions about how to evaluate a pulmonary nodule can affect patient satisfaction, distress, and adherence to evaluation. This article discusses the complexity of tailoring patient-provider communication, decision-making, and implementation of guidelines for pulmonary nodule evaluation to the individual patient, emphasizing the importance of how information is conveyed and the value of listening to and addressing patients' concerns. We summarize the relevant guideline recommendations and literature, and provide two case scenarios to illustrate a patient-centered approach to discussing and managing pulmonary nodules from our perspectives as a pulmonologist and thoracic surgeon.
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Causal directed acyclic graphs (cDAGs) have become popular tools for researchers to better examine biases related to causal questions. DAGs comprise a series of arrows connecting nodes that represent variables and in doing so can demonstrate the causal relation between different variables. cDAGs can provide researchers with a blueprint of the exposure and outcome relation and the other variables that play a role in that causal question. cDAGs can be helpful in the design and interpretation of observational studies in pulmonary, critical care, sleep, and cardiovascular medicine. They can also help clinicians and researchers to better identify the structure of different biases that can affect the validity of observational studies. ⋯ We use cDAGs and clinical examples that are mostly focused in the area of pulmonary medicine to describe the structure of confounding, selection bias, overadjustment bias, and detection bias. These principles are then applied to a more complex published case study on the use of statins and COPD mortality. We also introduce readers to other resources for a more in-depth discussion of causal inference principles.