To fail is human: remediating remediation in medical education.

IntroductionRemediating failing medical learners has traditionally been a craft activity responding to individual learner and remediator circumstances. Although there have been moves towards more systematic approaches to remediation (at least at the institutional level), these changes have tended to focus on due process and defensibility rather than on educational principles. As remediation practice evolves, there is a growing need for common theoretical and systems-based perspectives to guide this work.MethodsThis paper steps back from the practicalities of remediation practice to take a critical systems perspective on remediation in contemporary medical education. In doing so, the authors acknowledge the complex interactions between institutional, professional, and societal forces that are both facilitators of and barriers to effective remediation practices.ResultsThe authors propose a model that situates remediation within the contexts of society as a whole, the medical profession, and medical education institutions. They also outline a number of recommendations to constructively align remediation principles and practices, support a continuum of remediation practices, destigmatize remediation, and develop institutional communities of practice in remediation.DiscussionMedical educators must embrace a responsible and accountable systems-level approach to remediation if they are to meet their obligations to provide a safe and effective physician workforce

Characterizing steady states of genome-scale metabolic networks in continuous cell cultures

We present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. More importantly, we derive a number of consequences from the model that are independent of parameter values. First, that the ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties invariant across perfusion systems. This conclusion is robust even in the presence of multi-stability, which is explained in our model by the negative feedback loop on cell growth due to toxic byproduct accumulation. Moreover, a complex landscape of steady states in continuous cell culture emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced. Thus, in order to actually reflect the expected behavior in perfusion, performance benchmarks of cell-lines and culture media should be carried out in a chemostat

Six questions on the construction of ontologies in biomedicine

(Report assembled for the Workshop of the AMIA Working Group on Formal Biomedical Knowledge Representation in connection with AMIA Symposium, Washington DC, 2005.) Best practices in ontology building for biomedicine have been frequently discussed in recent years. However there is a range of seemingly disparate views represented by experts in the field. These views not only reflect the different uses to which ontologies are put, but also the experiences and disciplinary background of these experts themselves. We asked six questions related to biomedical ontologies to what we believe is a representative sample of ontologists in the biomedical field and came to a number conclusions which we believe can help provide an insight into the practical problems which ontology builders face today

A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT

Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. Results: Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for optimizations. The MOEA improvements were evaluated for example prostate cases with one target and two OARs. The modified MOEA dominated 11.3% of plans using a standard genetic algorithm package. By implementing domination advantage and protocol objectives, small diverse populations of clinically acceptable plans that were only dominated 0.2% by the Pareto front could be generated in a fraction of an hour. Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. It optimizes not only beamlet intensities but also objective function parameters on a patient-specific basis

Mathematical Models of the Impact of IL2 Modulation Therapies on T Cell Dynamics

Several reports in the literature have drawn a complex picture of the effect of treatments aiming to modulate IL2 activity in vivo. They seem to promote either immunity or tolerance, probably depending on the specific context, dose, and timing of their application. Such complexity might derive from the pleiotropic role of IL2 in T cell dynamics. To theoretically address the latter possibility, our group has developed several mathematical models for Helper, Regulatory, and Memory T cell population dynamics, which account for most well-known facts concerning their relationship with IL2. We have simulated the effect of several types of therapies, including the injection of: IL2; antibodies anti-IL2; IL2/anti-IL2 immune-complexes; and mutant variants of IL2. We studied the qualitative and quantitative conditions of dose and timing for these treatments which allow them to potentiate either immunity or tolerance. Our results provide reasonable explanations for the existent pre-clinical and clinical data, predict some novel treatments, and further provide interesting practical guidelines to optimize the future application of these types of treatments

Gravitation Physics at BGPL

We report progress on a program of gravitational physics experiments using cryogenic torsion pendula undergoing large-amplitude torsion oscillation. This program includes tests of the gravitational inverse square law and of the weak equivalence principle. Here we describe our ongoing search for inverse-square-law violation at a strength down to $10^{-5}$ of standard gravity. The low-vibration environment provided by the Battelle Gravitation Physics Laboratory (BGPL) is uniquely suited to this study.Comment: To be published in The Proceedings of the Francesco Melchiorri Memorial Conference as a special issue of New Astronomy Review

Factors influencing medical student attrition and their implications in a large multi-center randomized education trial

Participant attrition may be a significant threat to the generalizability of the results of educational research studies if participants who do not persist in a study differ from those who do in ways that can affect the experimental outcomes. A multi-center trial of the efficacy of different computer-based instructional strategies gave us the opportunity to observe institutional and student factors linked to attrition from a study and the ways in which they altered the participation profile. The data is from a randomized controlled trial conducted at seven US medical schools investigating the educational impact of different instructional designs for computer-based learning modules for surgical clerks. All students undertaking their surgical clerkships at the participating schools were invited participate and those that consented were asked to complete five study measures during their surgery clerkship. Variations in study attrition rates were explored by institution and by participants' self-regulation, self-efficacy, perception of task value, and mastery goal orientation measured on entry to the study. Of the 1,363 invited participants 995 (73 %) consented to participate and provided baseline data. There was a significant drop in the rate of participation at each of the five study milestones with 902 (94 %) completing at least one of two module post-test, 799 (61 %) both module post-tests, 539 (36 %) the mid-rotation evaluation and 252 (25 %) the final evaluation. Attrition varied between institutions on survival analysis (p < 0.001). Small but statistically significant differences in self-regulation (p = 0.01), self-efficacy (p = 0.02) and task value (p = 0.04) were observed but not in mastery or performance goal orientation measures (p = NS). Study attrition was correlated with lower achievement on the National Board of Medical Examiners subject exam. The results of education trials should be interpreted with the understanding that students who persist may be somewhat more self-regulated, self-efficacious and higher achievers than their peers who drop out and as such do not represent the class as a whole

Transmitter-side antennas correlation in SVD-assisted MIMO systems

MIMO techniques allow increasing wireless channel performance by decreasing the BER and increasing the channel throughput and in consequence are included in current mobile communication standards. MIMO techniques are based on benefiting the existence of multipath in wireless communications and the application of appropriate signal processing techniques. The singular value decomposition (SVD) is a popular signal processing technique which, based on the perfect channel state information (PCSI) knowledge at both the transmitter and receiver sides, removes inter-antenna interferences and improves channel performance. Nevertheless, the proximity of the multiple antennas at each front-end produces the so called antennas correlation effect due to the similarity of the various physical paths. In consequence, antennas correlation drops the MIMO channel performance. This investigation focuses on the analysis of a MIMO channel under transmitter-side antennas correlation conditions. First, antennas correlation is analyzed and characterized by the correlation coefficients. The analysis describes the relation between antennas correlation and the appearance of predominant layers which significantly affect the channel performance. Then, based on the SVD, pre- and post-processing is applied to remove inter-antenna interferences. Finally, bit- and power allocation strategies are applied to reach the best performance. The resulting BER reveals that antennas correlation effect diminishes the channel performance and that not necessarily all MIMO layers must be activated to obtain the best performance