Impairment Rating Ambiguity in the United States: The Utah Impairment Guides for Calculating Workers' Compensation Impairments

Since the implementation of workers' compensation, accurately and consistently rating impairment has been a concern for the employee and employer, as well as rating physicians. In an attempt to standardize and classify impairments, the American Medical Association (AMA) publishes the AMA Guides ("Guides"), and recently published its 6th edition of the AMA Guides. Common critiques of the AMA Guides 6th edition are that they are too complex, lacking in evidence-based methods, and rarely yield consistent ratings. Many states mandate use of some edition of the AMA Guides, but few states are adopting the current edition due to the increasing difficulty and frustration with their implementation. A clearer, simpler approach is needed. Some states have begun to develop their own supplemental guides to combat problems in complexity and validity. Likewise studies in Korea show that past methods for rating impairment are outdated and inconsistent, and call for measures to adapt current methods to Korea's specific needs. The Utah Supplemental Guides to the AMA Guides have been effective in increasing consistency in rating impairment. It is estimated that litigation of permanent impairment has fallen below 1% and Utah is now one of the least costly states for obtaining workers' compensation insurance, while maintaining a medical fee schedule above the national average. Utah's guides serve as a model for national or international impairment guides

Coronavirus Disease (COVID-19): The Value of Chest Radiography for Patients Greater Than Age 50 Years at an Earlier Timepoint of Symptoms Compared With Younger Patients

Background: A relative paucity of data exists regarding chest radiography (CXR) in diagnosis of coronavirus disease (COVID-19) compared to computed tomography. We address the use of a strict pattern of CXR findings for COVID-19 diagnosis, specifically during early onset of symptoms with respect to patient age. Methods: We performed a retrospective study of patients under investigation for COVID-19 who presented to the emergency department during the COVID-19 outbreak of 2020 and had CXR within 1 week of symptoms. Only reverse transcription polymerase chain reaction (RT-PCR)-positive patients were included. Two board-certified radiologists, blinded to RT-PCR results, assessed 60 CXRs in consensus and assigned 1 of 3 patterns: characteristic, atypical, or negative. Atypical patterns were subdivided into more suspicious or less suspicious for COVID-19. Results: Sixty patients were included: 30 patients aged 52 to 88 years and 30 patients aged 19 to 48 years. Ninety-three percent of the older group demonstrated an abnormal CXR and were more likely to have characteristic and atypical-more suspicious findings in the first week after symptom onset than the younger group. The relationship between age and CXR findings was statistically significant (chi(2) [2, n=60]=15.70; P=0.00039). The relationship between negative and characteristic COVID-19 CXR findings between the 2 age cohorts was statistically significant with Fisher exact test resulting in a P value of 0.001. Conclusion:COVID-19 positive patients \u3e50 years show earlier, characteristic patterns of statistically significant CXR changes than younger patients, suggesting that CXR is useful in the early diagnosis of infection. CXR can be useful in early diagnosis of COVID-19 in patients older than 50 years

LI-RADS treatment response assessment of combination locoregional therapy for HCC

HCC incidence continues to increase worldwide and is most frequently discovered at an advanced stage when limited curative options are available. Combination locoregional therapies have emerged to improve patient survival and quality of life or downstage patients to curative options. The increasing options for locoregional therapy combinations require an understanding of the expected post-treatment imaging appearance in order to assess treatment response. This review aims to describe the synergy between TACE combined with thermal ablation and TACE combined with SBRT. We will also illustrate expected imaging findings that determine treatment efficacy based on the mechanism of tissue injury using the LI-RADS Treatment Response Algorithm

Urinothorax: A rare complication of percutaneous nephrostomy

We present a case of a urinothorax resulting from treatment of genitourinary pathology. The presentation, diagnosis, and management of a 46-year-old female with an urinothorax are discussed. Urinothorax is a rare cause of a pleural effusion, most commonly arising from a traumatic etiology. Imaging can be crucial in the diagnosis, particularly computerized tomography (CT), which can help characterize any associated causative genitourinary abnormalities such as anatomical defects or a urinoma. A urinothorax is often posttraumatic in etiology, associated with the treatment of genitourinary pathology, as in this case. Treatment of the source of the urine leak is required to properly manage an urinothorax and often requires a multi-disciplinary approach. Keywords: Hospital Medicine, Nephrology, Pulmonary Diseases, Radiology, Urology, Interventional Radiolog

Medical Malpractice and Diagnostic Radiology: Challenges and Opportunities

Medicolegal challenges in radiology are broad and impact both radiologists and patients. Radiologists may be affected directly by malpractice litigation or indirectly due to defensive imaging ordering practices. Patients also could be harmed physically, emotionally, or financially by unnecessary tests or procedures. As technology advances, the incorporation of artificial intelligence into medicine will bring with it new medicolegal challenges and opportunities. This article reviews the current and emerging direct and indirect effects of medical malpractice on radiologists and summarizes evidence-based solutions

The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19 : A Geometric Transformation Perspective

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χ 2 McNemar′s statistic = 163.2 and a p-value of 2.23 × 10−37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.Applied Science, Faculty ofMedicine, Faculty ofNon UBCElectrical and Computer Engineering, Department ofRadiology, Department ofReviewedFacultyResearche

Connectionist psycholinguistics : capturing the empirical data

Connectionist psycholinguistics is an emerging approach to modeling empirical data on human language processing using connectionist computational architectures. For almost 20 years, connectionist models have increasingly been used to model empirical data across many areas of language processing. We critically review four key areas: speech processing, sentence processing, language production, and reading aloud, and evaluate progress against three criteria: data contact, task veridicality, and input representativeness. Recent connectionist modeling efforts have made considerable headway toward meeting these criteria, although it is by no means clear whether connectionist (or symbolic) psycholinguistics will eventually provide an integrated model of full-scale human language processing