A 1% TBSA Chart Reduces Math Errors While Retaining Acceptable First-Estimate Accuracy.

Life-threatening and treatment-altering errors occur in estimates of the percentage of total body surface area burned (%TBSA burned) with unacceptable frequency. In response, numerous attempts have been made to improve the charts commonly used for %TBSA-burned estimation. Recent research shows that the largest errors in %TBSA-burned estimates probably come from sources other than inaccurate values in the charts. Here, we develop a taxonomy of the possible sources of error and their impact on %TBSA-burned estimates. Also, we observe that different caregivers have different estimation needs: First-responders require a rapid estimate with sufficient accuracy to enable them to begin care and determine patient transport options, while burn surgeons ordering skin grafts desire accuracy to the square centimeter, and can afford considerable time to attain that accuracy. These competing needs suggest that a one-tool-fits-all-caregivers approach is suboptimal. We therefore present a validated, simplified burn chart that minimizes one of the largest sources of random errors in %TBSA-burned estimates-simple calculation errors-while also being quick and requiring little training. NCHart-1 also enables simple consensus estimates, as well as separation of estimation subtasks across caregivers, leading to several potential improvements in mass casualty situations. Our results demonstrate that NCHart-1 possesses the accuracy necessary for first responders, while reliably producing results in less than 2 minutes. Of 76 healthcare professionals surveyed, a large majority indicated a preference for NCHart-1 over their previous methods for ease of both use and training. For clinical or commercial use of NCHart-1, please contact: tech.commercialization@nationwidechildrens.org.

StickWRLD as an Interactive Visual Pre-Filter for Canceromics-Centric Expression Quantitative Trait Locus Data.

As datasets increase in complexity, the time required for analysis (both computational and human domain-expert) increases. One of the significant impediments introduced by such burgeoning data is the difficulty in knowing what features to include or exclude from statistical models. Simple tables of summary statistics rarely provide an adequate picture of the patterns and details of the dataset to enable researchers to make well-informed decisions about the adequacy of the models they are constructing. We have developed a tool, StickWRLD, which allows the user to visually browse through their data, displaying all possible correlations. By allowing the user to dynamically modify the retention parameters (both P and the residual, r), StickWRLD allows the user to identify significant correlations and disregard potential correlations that do not meet those same criteria - effectively filtering through all possible correlations quickly and identifying possible relationships of interest for further analysis. In this study, we applied StickWRLD to a semi-synthetic dataset constructed from two published human datasets. In addition to detecting high-probability correlations in this dataset, we were able to quickly identify gene-SNP correlations that would have gone undetected using more traditional approaches due to issues of low penetrance.