Background The purpose of this study was to evaluate patients' views of conflicts of interest (COI) and their comprehension of recent legislation known as the Physician Payments Sunshine Act. This report constitutes the first evaluation of plastic surgery patients' views on COI and the government-mandated Sunshine Act. Methods This cross-sectional study invited patients at an academic, general plastic surgery outpatient clinic to complete an anonymous survey. The survey contained 25 questions that assessed respondents' perceptions of physician COI and awareness of the Sunshine Act. Analyses were performed to examine whether perspectives on COI and the Sunshine Act varied by level of education or age. Results A total of 361 individuals completed the survey (90% response rate). More than half of respondents with an opinion believed that COI would affect their physician's clinical decision-making (n = 152, 52.9%). Although almost three fourths (n = 196, 71.2%) believed that COI should be regulated and COI information reported to a government agency, the majority were not aware of the Sunshine Act before this survey (n = 277, 81.2%) and had never accessed the database (n = 327, 95.9%). More than half of patients (n = 161, 59.2%) stated that they would access a publicly available database with physicians' COI information. A larger proportion of older and educated patients believed that regulation of physicians' COI was important (P < 0.001). Conclusions Awareness of and access to plastic surgeon COI information is low among plastic surgery patients. Older and more educated patients believed that transparency regarding COI is important with regard to their clinical care.
Objectives: To develop and validate a deep learning algorithm to automatically detect and segment an orbital abscess depicted on computed tomography (CT). Methods: We retrospectively collected orbital CT scans acquired on 67 pediatric subjects with a confirmed orbital abscess in the setting of infectious orbital cellulitis. A context-aware convolutional neural network (CA-CNN) was developed and trained to automatically segment orbital abscess. To reduce the requirement for a large dataset, transfer learning was used by leveraging a pre-trained model for CT-based lung segmentation. An ophthalmologist manually delineated orbital abscesses depicted on the CT images. The classical U-Net and the CA-CNN models with and without transfer learning were trained and tested on the collected dataset using the 10-fold cross-validation method. Dice coefficient, Jaccard index, and Hausdorff distance were used as performance metrics to assess the agreement between the computerized and manual segmentations. Results: The context-aware U-Net with transfer learning achieved an average Dice coefficient and Jaccard index of 0.78 AE 0.12 and 0.65 AE 0.13, which were consistently higher than the classical U-Net or the context-aware U-Net without transfer learning (P < 0.01). The average differences of the abscess between the computerized results and the experts in terms of volume and Hausdorff distance were 0.10 AE 0.11 mL and 1.94 AE 1.21 mm, respectively. The context-aware U-Net detected all orbital abscess without false positives. Conclusions:The deep learning solution demonstrated promising performance in detecting and segmenting orbital abscesses on CT images in strong agreement with a human observer.
Background: Traumatic brain injury (TBI) and Alzheimer's disease (AD) bear a complex relationship, potentially increasing risk of one another reciprocally. However, recent evidence suggests post-TBI dementia exists as a distinct neurodegenerative syndrome, confounding AD diagnostic accuracy in clinical settings. This investigation sought to evaluate TBI's impact on the accuracy of clinician-diagnosed AD using gold standard neuropathological criteria. Methods:In this preliminary analysis, data was acquired from the National Alzheimer's Coordinating Center (NACC), which aggregates clinical and neuropathologic information from Alzheimer's Disease Centers across the United States. Modified National Institute on Aging-Reagan criteria were applied to confirm AD by neuropathology.Results: Among participants with clinician-diagnosed AD, TBI history was associated with misdiagnosis (false positives) (OR = 1.351 (95% CI: 1.091 -1.674), p = 0.006). Among participants without clinician-diagnosed AD, TBI history was not associated with false negatives.Discussion: TBI moderates AD diagnostic accuracy. Possible AD misdiagnosis can mislead patients, influence treatment decisions, and confound research study designs. Further work examining the influence of TBI on dementia diagnosis is warranted.
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