A suite of software tools has been developed for dose estimation (BAT, WinFRAT) and prediction of acute health effects (WinFRAT, H-Module) using clinical symptoms and/or changes in blood cell counts. We constructed a database of 191 ARS cases using the METREPOL (n = 167) and the SEARCH-database (n = 24). The cases ranged from unexposed (RC0), to mild (RC1), moderate (RC2), severe (RC3), and lethal ARS (RC4). From 2015–2019, radiobiology students and participants of two NATO meetings predicted clinical outcomes (RC, H-ARS, and hospitalization) based on clinical symptoms. We evaluated the prediction outcomes using the same input datasets with a total of 32 teams and 94 participants. We found that: (1) unexposed (RC0) and mildly exposed individuals (RC1) could not be discriminated; (2) the severity of RC2 and RC3 were systematically overestimated, but almost all lethal cases (RC4) were correctly predicted; (3) introducing a prior education component for non-physicians significantly increased the correct predictions of RC, ARS, and hospitalization by around 10% (p<0.005) with a threefold reduction in variance and a halving of the evaluation time per case; (4) correct outcome prediction was independent of the software tools used; and (5) comparing the dose estimates generated by the teams with H-ARS severity reflected known limitations of dose alone as a surrogate for H-ARS severity. We found inexperienced personnel can use software tools to make accurate diagnostic and treatment recommendations with up to 98% accuracy. Educational training improved the quality of decision making and enabled participants lacking a medical background to perform comparably to experts.
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HemoDose is a software tool, which estimates absorbed doses based on blood cell counts (BCC). The aim of our study was to validate HemoDose for early dose estimates. Dose estimates generated by HemoDose were compared with dose estimates stored in SEARCH from radiation victims. Moreover, BCC from unirradiated donors and corresponding HemoDose dose estimates were analysed. We employed linear or logistic regression analysis. There was a significant correlation between calculated doses by HemoDose based on single and multiple lymphocyte counts when omitting lowest and highest dose estimates. Furthermore, there was a significant correlation between calculated doses by HemoDose based on lymphocyte counts and the estimated doses based on DIC. The dose estimates by HemoDose based on lymphocyte counts and DIC showed a comparable correlation with HARS degrees 0 and 4. In conclusion, HemoDose dose estimation based on early lymphocyte counts appears to be a promising biodosimetry tool under certain considerations.
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