医师资格考试存在的问题分析及对策探讨

医师资格考试对于提高医师的素质,保证医疗质量至关重要。通过专家访谈,了解目前医师资格考试存在的问题,并提出相应的管理对策,以促进医师资格考试制度不断完善。     

WHO health risk assessment process for static fields

The World Health Organization (WHO) has a commitment to helping Member States achieve safe, sustainable and health-enhancing human environments, protected from biological, chemical and physical agents. The latter includes advising on the health impact of electromagnetic fields (EMFs) and radiation.

The results of the WHO/ICNIRP/NRPB workshop on static magnetic fields, published in this volume, provide a valuable and much needed contribution to the health risk assessment of exposure to static electric and magnetic fields, which is currently being coordinated by the WHO's International EMF Project. This WHO health risk assessment will be published as an environmental health criteria (EHC) monograph in early 2005.

This paper briefly gives an overview of the process of developing the WHO static fields EHC monograph, the criteria applied to studies that could contribute to the EHC, along with the ‘weight-of-evidence’ approach to health risk assessment. In addition, there is an increasing awareness of the need to account for uncertainty in the science database. This is traditionally addressed by further research, and the EMF project addresses these needs through the development of a ‘research agenda’. However, research programmes may take several years to complete, and the long latency associated with diseases such as cancer in people may also preclude a rapid outcome in some studies. The issue of current uncertainty is being addressed by the WHO EMF project through the development of a ‘precautionary framework’ in which precautionary measures will be applied to policy recommendations.     

Virtual identification of essential proteins within the protein interaction network of yeast

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.     

How icosahedral are icosahedral clusters?

Icosahedral symmetry, not contemplated within the crystallographic space groups, is nevertheless present to a high degree of perfection in a variety of clusters, in molecular, ionic, covalent or metallic structures. The application of continuous shape measures to those structures allows for a quantification of the degree of icosahedral symmetry present in each case, a first necessary step for a deeper discussion of the factors that favor the adoption of the icosahedron as a stable structural motif. Examples analyzed include boranes and carbaboranes, main group, rare earth or transition metal clusters, ligand-bridged polynuclear complexes and sets of donor atoms in mono- or polynuclear complexes. Specific examples are found of structures that appear along the minimal distortion pathways from the icosahedron to the cuboctahedron or the anticuboctahedron.     

Exploring interactions by second-stage community analyses

Many biological data sets, from field observations and manipulative experiments, involve crossed factor designs, analysed in a univariate context by higher-way analyses of variance which partition out ‘main’ and ‘interaction’ effects. Indeed, tests for significance of interactions among factors, such as differing Before-After responses at Control and Impact sites, are the basis of the widely used BACI strategy for detecting impacts in the environment. There are difficulties, however, in generalising simple univariate definitions of interaction, from classic linear models, to the robust, non-parametric multivariate methods that are commonly required in handling assemblage data. The size of an interaction term, and even its existence at all, depends crucially on the measurement scale, so it is fundamentally a parametric construct. Despite this, certain forms of interaction can be examined using non-parametric methods, namely those evidenced by changing assemblage patterns over many time periods, for replicate sites from different experimental conditions (types of ‘Beyond BACI’ design) - or changing multivariate structure over space, at many observed times. Second-stage MDS, which can be thought of as an MDS plot of the pairwise similarities between MDS plots (e.g. of assemblage time trajectories), can be used to illustrate such interactions, and they can be formally tested by second-stage ANOSIM permutation tests. Similarities between (first-stage) multivariate patterns are assessed by rank-based matrix correlations, preserving the fully non-parametric approach common in marine community studies. The method is exemplified using time-series data on corals from Thailand, macrobenthos from Tees Bay, UK, and macroalgae from a complex recolonisation experiment carried out in the Ligurian Sea, Italy. The latter data set is also used to demonstrate how the analysis copes straightforwardly with certain repeated-measures designs.     

Goodness-of-fit diagnostics for Bayesian hierarchical models

This article proposes methodology for assessing goodness of fit in Bayesian hierarchical models. The methodology is based on comparing values of pivotal discrepancy measures (PDMs), computed using parameter values drawn from the posterior distribution, to known reference distributions. Because the resulting diagnostics can be calculated from standard output of Markov chain Monte Carlo algorithms, their computational costs are minimal. Several simulation studies are provided, each of which suggests that diagnostics based on PDMs have higher statistical power than comparable posterior-predictive diagnostic checks in detecting model departures. The proposed methodology is illustrated in a clinical application; an application to discrete data is described in supplementary material.     

Multilocus estimation of selfing and its heritability

We describe a new method of estimating the selfing rate (S) in a mixed mating population based on a population structure approach that accounts for possible intergenerational correlation in selfing rate, giving rise to an estimate of the upper limit for heritability of selfing rate (h(2)). A correlation between generations in selfing rate is shown to affect one- and two-locus probabilities of identity by descent. Conventional estimates of selfing rate based on a population structure approach are positively biased by intergenerational correlation in selfing. Multilocus genotypes of individuals are used to give maximum-likelihood estimates of S and h(2) in the presence of scoring artifacts. Our multilocus estimation of selfing rate and its heritability (MESH) method was tested with simulated data for a range of conditions. Selfing rate estimates from MESH have low bias and root mean squared error, whereas estimates of the heritability of selfing rate have more uncertainty. Increasing the number of individuals in a sample helps to reduce bias and root mean squared error more than increasing the number of loci of sampled individuals. Improved estimates of selfing rate, as well as estimates of its heritability, can be obtained with this method, although a large number of loci and individuals are needed to achieve best results.     

The effects of thinning and gypsy moth defoliation on wood volume growth in oaks

Stem dissection and dendroecological methods were used to examine the effects of thinning and defoliation by gypsy moth (Lymantria dispar L.) on wood volume increment in oaks (Quercus rubra L., Q. alba L., Q. prinus L.). A model was developed to evaluate radial volume increment growth at three time periods: before defoliation, during defoliation and after defoliation, as a function of species, defoliation intensity and crown position. Volume increment during these same time periods was also compared at different stem locations. Trees were defoliated for two consecutive years and results indicated that volume loss was greater during the second year of defoliation with complete recovery taking 2–3 years after defoliation. Oaks in thinned stands had similar reductions in annual volume increment during defoliation as those in the unthinned stand. Annual volume increment demonstrated a decreasing trend from stump to base of the live crown and volume increment of the lowest log (from stump height to 1.37 m), was always higher than upper log sections, even during defoliation. Both earlywood and latewood increments were reduced during defoliation; however, latewood reductions were distributed along entire stems while earlywood reductions were greater on upper stem sections within the crown.     

A signed-rank test for clustered data

Summary .   We consider the problem of comparing two outcome measures when the pairs are clustered. Using the general principle of within-cluster resampling, we obtain a novel signed-rank test for clustered paired data. We show by a simple informative cluster size simulation model that only our test maintains the correct size under a null hypothesis of marginal symmetry compared to four other existing signed rank tests; further, our test has adequate power when cluster size is noninformative. In general, cluster size is informative if the distribution of pair-wise differences within a cluster depends on the cluster size. An application of our method to testing radiation toxicity trend is presented.