Learning optimal opioid prescribing and monitoring: a simulation study of medical residents.

OBJECTIVE: Hospitalized patients often receive opioids. There is a lack of consensus regarding evidence-based guidelines or training programs for effective management of pain in the hospital. We investigated the viability of using an Internet-based opioid dosing simulator to teach residents appropriate use of opioids to treat and manage acute pain. MATERIALS AND METHODS: We used a prospective, longitudinal design to evaluate the effects of simulator training. In face-to-face didactic sessions, we taught 120 (108 internal medicine and 12 family medicine) residents principles of pain management and how to use the simulator. Each trainee completed 10 training and, subsequently, 5 testing trials on the simulator. For each trial, we collected medications, doses, routes and times of administration, pain scores, and a summary score. We used mixed-effects regression models to assess the impact of simulation training on simulation performance scores, variability in pain score trajectories, appropriate use of short- and long-acting opioids, and use of naloxone. RESULTS: Trainees completed 1582 simulation trials (M = 13.2, SD = 6.8), with sustained improvements in their simulated pain management practices. Over time, trainees improved their overall simulated pain management scores (b = 0.05, P < .01), generated lower pain score trajectories with less variability (b = -0.02, P < .01), switched more rapidly from short-acting to long-acting agents (b = -0.50, P < .01), and used naloxone less often (b = -0.10, P < .01). DISCUSSION AND CONCLUSIONS: Trainees translated their understanding of didactically presented principles of pain management to their performance on simulated patient cases. Simulation-based training presents an opportunity for improving opioid-based inpatient acute pain management.

IAEA Isotope-enabled coupled catchment-lake water balance model, IWBMIso: description and validation.

The International Atomic Energy Agency (IAEA) Water Balance Model with Isotopes (IWBMIso) is a spatially distributed monthly water balance model that considers water fluxes and storages and their associated isotopic compositions. It is composed of a lake water balance model that is tightly coupled with a catchment water balance model. Measured isotope compositions of precipitation, rivers, lakes, and groundwater provide data that can be used to make an improved estimate of the magnitude of the fluxes among the model components. The model has been developed using the Object Modelling System (OMS). A variety of open source geographic information systems and web-based tools have been combined to provide user support for (1) basin delineation, characterization, and parameterization; (2) data pre-processing; (3) model calibration and application; and (4) visualization and analysis of model results. In regions where measured data are limited, the model can use freely available global data sets of climate, isotopic composition of precipitation, and soils and vegetation characteristics to create input data files and estimate spatially distributed model parameters. The OMS model engine and support functions, and the spatial and web-based tool set are integrated using the Colorado State University Environmental Risk Assessment and Management System (eRAMS) framework. The IWBMIso can be used to assess the spatial and temporal variability of annual and monthly water balance components for input to water planning and management.

A cold and slow molecular beam.

Employing a two-stage cryogenic buffer gas cell, we produce a cold, hydrodynamically extracted beam of calcium monohydride molecules with a near effusive velocity distribution. Beam dynamics, thermalization and slowing are studied using laser spectroscopy. The key to this hybrid, effusive-like beam source is a "slowing cell" placed immediately after a hydrodynamic, cryogenic source [Patterson et al., J. Chem. Phys., 2007, 126, 154307]. The resulting CaH beams are created in two regimes. In one regime, a modestly boosted beam has a forward velocity of v(f) = 65 m s(-1), a narrow velocity spread, and a flux of 10(9) molecules per pulse. In the other regime, our slowest beam has a forward velocity of v(f) = 40 m s(-1), a longitudinal temperature of 3.6 K, and a flux of 5 × 10(8) molecules per pulse.

Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments.

Modern methods for imaging the human brain, such as functional magnetic resonance imaging (fMRI) present a range of challenging statistical problems. In this paper, we first develop a large sample based test for between group comparisons and use it to determine the necessary sample size in order to obtain a target power via simulation under various alternatives for a given pre-specified significance level. Both testing and sample size calculations are particularly critical for neuroscientists who use these new techniques, since each subject is expensive to image.

Estimation and Classification of BOLD Responses Over Multiple Trials.

In this article, we model functional magnetic resonance imaging (fMRI) data for event-related experiment data using a fourth degree spline to fit voxel specific blood oxygenation level-dependent (BOLD) responses. The data are preprocessed for removing long term temporal components such as drifts using wavelet approximations. The spatial dependence is incorporated in the data by the application of 3D Gaussian spatial filter. The methodology assigns an activation score to each trial based on the voxel specific characteristics of the response curve. The proposed procedure has a capability of being fully automated and it produces activation images based on overall scores assigned to each voxel. The methodology is illustrated on real data from an event-related design experiment of visually guided saccades (VGS).

Estimation and classification of fMRI hemodynamic response patterns.

In this paper, we propose an approach to modeling functional magnetic resonance imaging (fMRI) data that combines hierarchical polynomial models, Bayes estimation, and clustering. A cubic polynomial is used to fit the voxel time courses of event-related design experiments. The coefficients of the polynomials are estimated by Bayes estimation, in a two-level hierarchical model, which allows us to borrow strength from all voxels. The voxel-specific Bayes polynomial coefficients are then transformed to the times and magnitudes of the minimum and maximum points on the hemodynamic response curve, which are in turn used to classify the voxels as being activated or not. The procedure is demonstrated on real data from an event-related design experiment of visually guided saccades and shown to be an effective alternative to existing methods.