Surgical resident experience on most trauma services is heavily weighted to nonoperative management, with a relatively low number of procedures, little experience with DPL, and highly variable experience with ultrasound. These data have serious implications for resident training and recruitment into the specialty.

Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients under standard-of-care conditions that may be exploitable to this end. However, most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control. Here, we propose a use of historical data that exploits linear covariate adjustment to improve the efficiency of trial analyses without incurring bias. Specifically, we train a prognostic model on the historical data, then estimate the treatment effect using a linear regression while adjusting for the trial subjects' predicted outcomes (their prognostic scores). We prove that, under certain conditions, this prognostic covariate adjustment procedure attains the minimum variance possible among a large class of estimators. When those conditions are not met, prognostic covariate adjustment is still more efficient than raw covariate adjustment and the gain in efficiency is proportional to a measure of the predictive accuracy of the prognostic model. We demonstrate the approach using simulations and a reanalysis of an Alzheimer's Disease clinical trial and observe meaningful reductions in mean-squared error and the estimated variance. Lastly, we provide a simplified formula for asymptotic variance that enables power and sample size calculations that account for the gains from the prognostic model for clinical trial design.

In this work we propose, implement, and test various optimizations of the typical energy grid-cross section pair lookup algorithm in Monte Carlo particle transport codes. The key feature common to all of the optimizations is a reduction in the length of the vector of energies that must be searched when locating the index of a particle's current energy. Other factors held constant, a reduction in energy vector length yields a reduction in CPU time. The computational methods we present here are physics-informed. That is, they are designed to utilize the physical information embedded in a simulation in order to reduce the length of the vector to be searched. More specifically, the optimizations take advantage of information about scattering kinematics, neutron cross section structure and data representation, and also the expected characteristics of a system's spatial flux distribution and energy spectrum. The methods that we present are implemented in the OpenMC Monte Carlo neutron transport code as part of this work. The gains in computational efficiency, as measured by overall code speedup, associated with each of the optimizations are demonstrated in both serial and multithreaded simulations of realistic systems. Depending on the system, simulation parameters, and optimization method employed, overall code speedup factors of 1.2 − 1.5, relative to the typical single-nuclide binary search algorithm, are routinely observed.

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In this preliminary investigation we demonstrate on-the-fly computation of unresolved resonance region cross sections. The on-the-fly method is implemented and tested in the OpenMC Monte Carlo neutron transport code. Preliminary results indicate that, in simulations of a system that is known to be highly sensitive to the e↵ects of resonance structure in unresolved region cross sections, the on-the-fly treatment produces results that are in excellent agreement with those produced with the well-established probability table method. Additionally, we use the on-the-fly approach to show that accounting for the resonance structure of the competitive inelastic scattering reaction cross section can have non-negligible e↵ects for an intermediate spectrum system. Comparisons between di↵erential reaction rates and k e↵ eigenvalues obtained using infinite-dilute, probability table, and onthe-fly cross sections in simulations of the Big Ten critical assembly are presented in this initial study.

In this work, we present the derivation and investigation of a new Doppler broadening rejection sampling approach for the exact treatment of resonance elastic scattering in Monte Carlo neutron transport codes. Implemented in OpenMC, this method correctly accounts for the energy dependence of cross sections when treating the thermal motion of target nuclei in elastic scattering events. The method is verified against both stochastic and deterministic reference results in the literature for 238 U resonance scattering. Upscatter percentages and mean scattered energies calculated with the method are shown to agree well with the reference scattering kernel results. Additionally, pin cell and full core k eff results calculated with this implementation of the exact resonance scattering kernel are shown to be in close agreement with those in the literature. The attractiveness of the method stems from its improvement upon a computationally expensive rejection sampling procedure employed by an earlier stochastic resonance scattering treatment. With no loss in accuracy, the accelerated resonant target velocity sampling algorithm is shown to reduce overall runtime by 3-5% relative to the Doppler broadening rejection correction method for both pin cell and full core benchmark problems. This translates to a 30-40% reduction in runtime overhead.

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