Complementary and conventional medicine in Switzerland: comparing characteristics of general practitioners

OBJECTIVES: Do structural characteristics of general practitioners (GPs) who practice complementary medicine (CAM) differ from those GPs who do not? Assessed characteristics included experience and professional integration of general practitioners (GPs), workload, medical activities, and personal and technical resources of practices. The investigated CAM disciplines were anthroposophic medicine, homoeopathy, traditional Chinese medicine, neural therapy and herbal medicine. MATERIAL AND METHODS: We designed a cross-sectional study with convenience and stratified samples of GPs providing conventional (COM) and/or complementary primary care in Switzerland. The samples were taken from the database of the Swiss medical association (FMH) and from CAM societies. Data were collected using a postal questionnaire. RESULTS: Of the 650 practitioners who were included in the study, 191 were COM, 167 noncertified CAM and 292 certified CAM physicians. The proportion of females was higher in the population of CAM physicians. Gender-adjusted age did not differ between CAM and COM physicians. Nearly twice as many CAM physicians work part-time. Differences were also seen for the majority of structural characteristics such as qualification of physicians, type of practice, type of staff, and presence of technical equipment. CONCLUSION: The study results show that structural characteristics of primary health care do differ between CAM and COM practitioners. We assumed that the activities of GPs are defined essentially by analyzed structures. The results are to be considered for evaluations in primary health care, particularly when quality of health care is assessed.

Statistical finite element model for bone shape and biomechanical properties

We present a framework for statistical finite element analysis combining shape and material properties, and allowing performing statistical statements of biomechanical performance across a given population. In this paper, we focus on the design of orthopaedic implants that fit a maximum percentage of the target population, both in terms of geometry and biomechanical stability. CT scans of the bone under consideration are registered non-rigidly to obtain correspondences in position and intensity between them. A statistical model of shape and intensity (bone density) is computed by means of principal component analysis. Afterwards, finite element analysis (FEA) is performed to analyse the biomechanical performance of the bones. Realistic forces are applied on the bones and the resulting displacement and bone stress distribution are calculated. The mechanical behaviour of different PCA bone instances is compared.

Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Alternative methods to kyphoplasty: vertebroplasty — lordoplasty

Percutaneous cement augmentation (vertebroplasty) was used for the first time in the 1980s, primarily for treatment of vertebral hemangioma [Galibert 1987]. It was only in the middle of the 1990s that it was used for treatment of metastases and increasingly for osteoporotic fractures of the spine [Cotten 1996; Weill 1996; Jensen 1997; Cortet 1999; Heini 2000]. In the meantime, this method has become established for treatment of painful osteoporotic fractures and for tumorous osteolysis of the spine. The clinical success rate is very high, with rapid pain relief in 70–90% of treated patients [Legroux-Gerot 2004; Zoarski 2002; Peh 2002; Barr 2000].

STrengthening the REporting of Genetic Association studies (STREGA)--an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

STrengthening the REporting of Genetic Association Studies (STREGA)--an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the strengthening the reporting of observational studies in epidemiology (STROBE) statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence, the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association (STREGA) studies initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

STrengthening the REporting of Genetic Association studies (STREGA): an extension of the STROBE Statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information into the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and issues of data volume that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE Statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Bayesian statistics in oncology: a guide for the clinical investigator

The rise of evidence-based medicine as well as important progress in statistical methods and computational power have led to a second birth of the >200-year-old Bayesian framework. The use of Bayesian techniques, in particular in the design and interpretation of clinical trials, offers several substantial advantages over the classical statistical approach. First, in contrast to classical statistics, Bayesian analysis allows a direct statement regarding the probability that a treatment was beneficial. Second, Bayesian statistics allow the researcher to incorporate any prior information in the analysis of the experimental results. Third, Bayesian methods can efficiently handle complex statistical models, which are suited for advanced clinical trial designs. Finally, Bayesian statistics encourage a thorough consideration and presentation of the assumptions underlying an analysis, which enables the reader to fully appraise the authors' conclusions. Both Bayesian and classical statistics have their respective strengths and limitations and should be viewed as being complementary to each other; we do not attempt to make a head-to-head comparison, as this is beyond the scope of the present review. Rather, the objective of the present article is to provide a nonmathematical, reader-friendly overview of the current practice of Bayesian statistics coupled with numerous intuitive examples from the field of oncology. It is hoped that this educational review will be a useful resource to the oncologist and result in a better understanding of the scope, strengths, and limitations of the Bayesian approach.

Presence of the Gpr179(nob5) allele in a C3H-derived transgenic mouse

PURPOSE To identify the mutation responsible for an abnormal electroretinogram (ERG) in a transgenic mouse line (tg21) overexpressing erythropoietin (Epo). The tg21 line was generated on a mixed (C3H; C57BL/6) background and lacked the b-wave component of the ERG. This no-b-wave (nob) ERG is seen in other mouse models with depolarizing bipolar cell (DBC) dysfunction and in patients with the complete form of congenital stationary night blindness (cCSNB). We determined the basis for the nob ERG phenotype and screened C3H mice for the mutation to evaluate whether this finding is important for the vision research community. METHODS ERGs were used to examine retinal function. The retinal structure of the transgenic mice was investigated using histology and immunohistochemistry. Inverse PCR was performed to identify the insertion site of the Epo transgene in the mouse genome. Affected mice were backcrossed to follow the inheritance pattern of the nob ERG phenotype. Quantitative real-time PCR (qRT PCR), Sanger sequencing, and immunohistochemistry were used to identify the mutation causing the defect. Additional C3H sublines were screened for the detected mutation. RESULTS Retinal histology and blood vessel structure were not disturbed, and no loss of DBCs was observed in the tg21 nob mice. The mutation causing the nob ERG phenotype is inherited independently of the tg21 transgene. The qRT PCR experiments revealed that the nob ERG phenotype reflected a mutation in Gpr179, a gene involved in DBC signal transduction. PCR analysis confirmed the presence of the Gpr179(nob5) insertional mutation in intron 1 of Gpr179. Screening for mutations in other C3H-derived lines revealed that C3H.Pde6b(+) mice carry the Gpr179 (nob5) allele whereas C3H/HeH mice do not. CONCLUSIONS We identified the presence of the Gpr179(nob5) mutation causing DBC dysfunction in a C3H-derived transgenic mouse line. The nob phenotype is not related to the presence of the transgene. The Gpr179(nob5) allele can be added to the list of background alleles that impact retinal function in commonly used mouse lines. By providing primers to distinguish between Gpr179 mutant and wild-type alleles, this study allows investigators to monitor for the presence of the Gpr179(nob5) mutation in other mouse lines derived from C3H.

Demographic situation in operative medicine in Switzerland - time to react?

Objective: The decreasing proportion of physicians of Swiss origin and the increasing number of part-time jobs in operative medicine might lead to a shortage of physicians in operative disciplines in Switzerland. The objective of the present study was to analyze the current demographic situation in operative medicine in Switzerland. Methods: During the summer of 2011, a 19-item anonymous electronic questionnaire was mailed to all directors of departments in operative medicine in Switzerland. The questionnaire was designed to gather data about the characteristics of the participating departments, the demographics (including the appointment (consultant, attending or resident), the proportion of female and foreign physicians, the latter’s origin, and the number of part-time jobs with a working time between 20 and 90%), and the proportion of vacant posts. Results: Of 775 questionnaires mailed to all directors of departments in operative medicine in Switzerland, 183 (24%) were returned. Overall, 40% were female, and 42% foreign physicians. The proportion of part-time jobs amounted to 17%. Vacant posts were found in 2%. Conclusions: An expansion of study places at the medical universities and of the incentives for the incumbents in operative medicine is necessary to avert a shortage of physicians in Switzerland.