Virtopsy-postmortem multislice computed tomography (MSCT) and magnetic resonance imaging (MRI) in a fatal scuba diving incident

The body of a 44-year-old scuba diver was examined using postmortem multislice computed tomography (MSCT) and magnetic resonance imaging (MRI), and findings were verified by subsequent autopsy. The goal was to find out whether the important pathomorphological findings for the reconstruction of events and the identification of cause and manner of death could be identified using modem digital cross-sectioning techniques. The findings of a massive vital decompression with pulmonary barotrauma and lethal gas embolism were identified in the radiological images. MSCT and MRI were superior to autopsy in the demonstration of the extent and distribution of gas accumulation in intraparenchymal blood vessels of internal organs as well as in areas of the body inaccessible by standard autopsy.     

Stochastic models of the Social Security trust funds

Each year in March, the Board of Trustees of the Social Security trust funds reports on the current and projected financial condition of the Social Security programs. Those programs, which pay monthly benefits to retired workers and their families, to the survivors of deceased workers, and to disabled workers and their families, are financed through the Old-Age, Survivors, and Disability Insurance (OASDI) Trust Funds. In their 2003 report, the Trustees present, for the first time, results from a stochastic model of the combined OASDI trust funds. Stochastic modeling is an important new tool for Social Security policy analysis and offers the promise of valuable new insights into the financial status of the OASDI trust funds and the effects of policy changes. The results presented in this article demonstrate that several stochastic models deliver broadly consistent results even though they use very different approaches and assumptions. However, they also show that the variation in trust fund outcomes differs as the approach and assumptions are varied. Which approach and assumptions are best suited for Social Security policy analysis remains an open question. Further research is needed before the promise of stochastic modeling is fully realized. For example, neither parameter uncertainty nor variability in ultimate assumption values is recognized explicitly in the analyses. Despite this caveat, stochastic modeling results are already shedding new light on the range and distribution of trust fund outcomes that might occur in the future.