Ophthalmology
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Persistent Opioid Use after Ophthalmic Surgery in Opioid-Naive Patients and Associated Risk Factors.
To determine the rate and risk factors for new persistent opioid use after ophthalmic surgery in the United States. ⋯ Exposure to opioids in the perioperative period is associated with new persistent use in patients who were previously opioid-naive. This suggests that exposure to opioids is an independent risk factor for persistent use in patients undergoing incisional ophthalmic surgery. Surgeons should be aware of those risks to identify at-risk patients given the current national opioid crisis and to minimize prescribing opioids when possible.
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To develop an agreed upon set of outcomes known as a "core outcome set" (COS) for noninfectious uveitis of the posterior segment (NIU-PS) clinical trials. ⋯ This study builds on international work across the clinical trials community and our qualitative research to construct the world's first COS for NIU-PS. The COS provides a list of outcomes that represent the priorities of key stakeholders and provides a minimum set of outcomes for use in all future NIU-PS clinical trials. Adoption of this COS can improve the value of future uveitis clinical trials and reduce noninformative research. Some of the outcomes identified do not yet have internationally agreed upon methods for measurement and should be the subject of future international consensus development.
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To illustrate what is inside the so-called black box of deep learning models (DLMs) so that clinicians can have greater confidence in the conclusions of artificial intelligence by evaluating adversarial explanation on its ability to explain the rationale of DLM decisions for glaucoma and glaucoma-related findings. Adversarial explanation generates adversarial examples (AEs), or images that have been changed to gain or lose pathologic characteristic-specific traits, to explain the DLM's rationale. ⋯ Adversarial explanation increased the explainability over GradCAM, a conventional heatmap-based explanation method. Adversarial explanation may help medical professionals understand more clearly the rationale of DLMs when using them for clinical decisions.