Evaluating New Cardiovascular Risk Factors for Risk Stratification

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73900/1/j.1751-7176.2008.07814.x.pd

Alternatives to the ROC Curve AUC and C-statistic for Risk Prediction Models

Assessment of risk prediction models has primarily utilized measures of discrimination, the ROC curve AUC and C-statistic. These derive from the risk distributions of patients and nonpatients, which in turn are derived from a population risk distribution. As greater dispersion of the population risk distribution produces greater separation of patient and nonpatient risks (discrimination), its parameters can be used as alternatives to the ROC curve AUC and C-statistic. Here continuous probability distributions are employed to develop insight into the relationship between their parameters and the ROC curve AUC and C-statistic derived from them. The ROC curve AUC and C-statistic are shown to have a straight-line relationship with the SD for uniform, half-sine, and symmetric triangular probability distributions, with slight differences in the slope: AUC approx 1/2+0.28 SD/(mean(1-mean)). This also characterizes the beta distribution over the same range of SD's. But at larger beta distribution SD's the plot of AUC versus SD deviates downward from this straight-line relationship, approaching the ROC curve AUC and SD of a perfect model (AUC=1, SD= $\sqrt{\rm mean(1-mean)}$). A simpler and more intuitive discrimination metric is the coefficient of discrimination, the difference between the mean risk in patients and nonpatients. This is SD2/(mean(1-mean)), which is also the same for any distribution. Since estimating parameters or metrics discards information, the population risk distribution should always be presented. As the ROC curve AUC and C-statistic are functions of this distribution's parameters, the parameters represent simpler, intuitive alternatives to these discrimination metrics. Among discrimination metrics, the coefficient of discrimination provides a simple, intuitive alternative to the ROC curve AUC and C-statistic.Comment: log likelihood text and figure adde

Neuroscience and research on learning and instruction: what kind of knowledge contributes to educational outcome?

Neurowissenschaftliche Ergebnisse besitzen für sich genommen keine Bedeutung für die Gestaltung schulischer Lerngelegenheiten. Die Methoden der Hirnforschung eignen sich weder dazu, Wissensunterschiede zwischen den Lernenden aufzudecken, noch geben sie Anleitung für die Darbietung von Informationen. Ein zukünftiges Potenzial neurowissenschaftlicher Methoden liegt jedoch in der Aufdeckung von Unterschieden in der Informationsverarbeitung, die sich auf der Verhaltensebene nicht beobachten lassen. (DIPF/Orig.)Strictly speaking, results from neuroscience can neither inform educational practice nor can they tell how to design learning environments. Brain imaging methods do not allow drawing conclusions on individual differences in knowledge representation and on appropriate information presentation. However, a future potential of brain imaging is the uncovering of differences in information processing that do not become apparent in behavior. (DIPF/Orig.

Construct Validity of Cognitive Reserve in a Multiethnic Cohort: The Northern Manhattan Study

Cognitive reserve is a hypothetical construct that has been used to inform models of cognitive aging and is presumed to be indicative of life experiences that may mitigate the effects of brain pathology. The purpose of this study was to evaluate the construct validity of cognitive reserve by examining both its convergent and its discriminant validity across three different samples of participants using structural equation modeling. The cognitive reserve variables were found to correlate highly with one another (thereby providing evidence of convergent validity), but demanding tests of discriminant validity indicated that, in two of the samples, the cognitive reserve construct was highly related to an executive functioning construct

Inventory for the assessment of representational competence of vector fields

Representational competence is essential for the acquisition of conceptual understanding in physics. It enables the interpretation of diagrams, graphs, and mathematical equations, and relating these to one another as well as to observations and experimental outcomes. In this study, we present the initial validation of a newly developed cross-contextual assessment of students’ competence in representing vector-field plots and field lines, the most common visualization of the concept of vector fields. The Representational Competence of Fields Inventory (RCFI) consists of ten single choice items and two items that each contain three true or false questions. The tool can be easily implemented within an online assessment. It assesses the understanding of the conventions of interpreting field lines and vector-field plots, as well as the translation between these. The intended use of the tool is both to scale students’ representational competences in respect to representations of vector fields and to reveal related misconceptions (areas of difficulty). The tool was administered at three German-speaking universities in Switzerland and Germany to a total of 515 first- and third-semester students from science, technology, engineering, and mathematics subjects. In these first steps of the validation of the RCFI, we evaluated its psychometric quality via classical test theory in combination with Rasch scaling and examined its construct validity by conducting student interviews. The RCFI exhibits a good internal consistency of ω ¼ 0.86, and the results of the Rasch analysis revealed that the items discriminate well among students from lower to medium-high competence levels. The RCFI revealed several misunderstandings and shortcomings, such as the confusion of the conventions for representing field lines and vector-field plots. Moreover, it showed that many students believed that field lines must not exhibit a curvature, that the lengths of field lines matter, and that field lines may have sharp corners. In its current version, the RCFI allows assessing students’ competence to interpret field representations, a necessary prerequisite for learning the widespread concept of vector fields. We report on planned future adaptations of the tool, such as optimizing some of the current distractors

Dephasing in Metals by Two-Level Systems in the 2-Channel-Kondo Regime

We point out a novel, non-universal contribution to the dephasing rate 1/\tau_\phi \equiv \gamma_\phi of conduction electrons in metallic systems: scattering off non-magnetic two-level systems (TLSs) having almost degenerate Kondo ground states. In the regime \Delta_{ren} < T < T_K (\Delta_{ren} = renormalized level splitting, T_K = Kondo temperature), such TLSs exhibit non-Fermi-liquid physics that can cause \gamma_\phi, which generally decreases with decreasing T, to seemingly saturate in a limited temperature range before vanishing for T \to 0. This could explain the saturation of dephasing recently observed in gold wires [Mohanty et al. Phys. Rev. Lett. 78, 3366 (1997)].Comment: Final published version, including minor improvements suggested by referees. 4 pages, Revtex, 1 figur

Is weak temperature dependence of electron dephasing possible?

The first-principle theory of electron dephasing by disorder-induced two state fluctuators is developed. There exist two mechanisms of dephasing. First, dephasing occurs due to direct transitions between the defect levels caused by inelastic electron-defect scattering. The second mechanism is due to violation of the time reversal symmetry caused by time-dependent fluctuations of the scattering potential. These fluctuations originate from an interaction between the dynamic defects and conduction electrons forming a thermal bath. The first contribution to the dephasing rate saturates as temperature decreases. The second contribution does not saturate, although its temperature dependence is rather weak, $\propto T^{1/3}$. The quantitative estimates based on the experimental data show that these mechanisms considered can explain the weak temperature dependence of the dephasing rate in some temperature interval. However, below some temperature dependent on the model of dynamic defects the dephasing rate tends rapidly to zero. The relation to earlier studies of the dephasing caused by the dynamical defects is discussed.Comment: 14 pages, 6 figures, submitted to PR