The credit risk(+) model with general sector correlations

We consider an enhancement of the credit risk+ model to incorporate correlations between sectors. We model the sector default rates as linear combinations of a common set of independent variables that represent macro-economic variables or risk factors. We also derive the formula for exact VaR contributions at the obligor level

The credit risk + model with general sector correlations

Credit risk + , Compound gamma distribution, Value at risk, Risk contribution, Correlation, Portfolio loss distribution, Moment generating function,

Humeral head hemiarthroplasty for patients with avascular necrosis following a solid organ transplant: A report of three shoulders

Background: Avascular necrosis (AVN) is a common occurrence following solid organ transplant (SOT) due to chronic steroid use. We report on three shoulders of humeral head hemiarthroplasty (HHA) in the setting of AVN after SOT. This topic is an important issue to study as there is limited literature on AVN requiring shoulder arthroplasty following SOT. As the prevalence of SOT continues to rise, it is important to study this population before making guidelines in the future. Case presentations: In one shoulder, a 51-year-old male presented with AVN of the left shoulder three years after undergoing a lung transplant and taking 5mg oral prednisone daily. In the second and third shoulder, a 61-year-old female presented with AVN of bilateral shoulders two years after undergoing a kidney transplant and taking 7.5mg oral prednisone daily. All three shoulders went on to have HHA with improved outcomes after surgery at a mean follow-up of 4.6 years. Conclusion: Shoulder arthroplasty is safe and effective for patients with avascular necrosis after a SOT

Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography

Abstract Background Automated identification of spectral domain optical coherence tomography (SD-OCT) features can improve retina clinic workflow efficiency as they are able to detect pathologic findings. The purpose of this study was to test a deep learning (DL)-based algorithm for the identification of Idiopathic Full Thickness Macular Hole (IFTMH) features and stages of severity in SD-OCT B-scans. Methods In this cross-sectional study, subjects solely diagnosed with either IFTMH or Posterior Vitreous Detachment (PVD) were identified excluding secondary causes of macular holes, any concurrent maculopathies, or incomplete records. SD-OCT scans (512 × 128) from all subjects were acquired with CIRRUS™ HD-OCT (ZEISS, Dublin, CA) and reviewed for quality. In order to establish a ground truth classification, each SD-OCT B-scan was labeled by two trained graders and adjudicated by a retina specialist when applicable. Two test sets were built based on different gold-standard classification methods. The sensitivity, specificity and accuracy of the algorithm to identify IFTMH features in SD-OCT B-scans were determined. Spearman’s correlation was run to examine if the algorithm’s probability score was associated with the severity stages of IFTMH. Results Six hundred and one SD-OCT cube scans from 601 subjects (299 with IFTMH and 302 with PVD) were used. A total of 76,928 individual SD-OCT B-scans were labeled gradable by the algorithm and yielded an accuracy of 88.5% (test set 1, 33,024 B-scans) and 91.4% (test set 2, 43,904 B-scans) in identifying SD-OCT features of IFTMHs. A Spearman’s correlation coefficient of 0.15 was achieved between the algorithm’s probability score and the stages of the 299 (47 [15.7%] stage 2, 56 [18.7%] stage 3 and 196 [65.6%] stage 4) IFTMHs cubes studied. Conclusions The DL-based algorithm was able to accurately detect IFTMHs features on individual SD-OCT B-scans in both test sets. However, there was a low correlation between the algorithm’s probability score and IFTMH severity stages. The algorithm may serve as a clinical decision support tool that assists with the identification of IFTMHs. Further training is necessary for the algorithm to identify stages of IFTMHs

Students' participation in collaborative research should be recognised

Letter to the editor