Right-left discrimination among medical students: Getting it right all of the time?

Objective To determine medical students’ self awareness and ability to discriminate right from left; to identify characteristics associated with this ability; and to identify any techniques used to aid discrimination. Design Questionnaire and psychometric study. Setting Undergraduate medical school, Northern Ireland. Participants 290 first year undergraduate students. Main outcome measure Medical students’ ability to discriminate right from left using the Bergen right-left discrimination test. Results Test scores ranged from 31 to 143 on a scale of 0- 144 (mean 112 (standard deviation 22.2)). Male students significantly outperformed female students (117.18 (26.96) v 110.80 (28.94)). Students who wanted to be surgeons performed significantly better than those who wanted to be general practitioners or medical doctors (119.87 (25.15) v 110.55 (27.36) v 112.50 (26.88)). The interaction effect for sex and career wishes was not significant (P=0.370). Students who used learnt techniques to help them discriminate scored significantly less than those who did not (P

Cinderella comes to the Ball: Article 14 and the right to non-discrimination in the ECHR

Article 14 ECHR has often been derided as a Cinderella provision, but during the last few years, this has started to change. This article examines how Article 14 has developed, and may live up to its potential as a powerful non-discrimination principle. The case law developments in relation to the “ambit” requirement in Article 14, the development of indirect discrimination case law, and the approval of positive action, all point to a more substantive conception of equality, which offers protection to disadvantaged and vulnerable groups.

Multivariate Statistical Analysis Applied to an IL6 Signal Transduction Model in Hepatocytes

This paper introduces the application of linear multivariate statistical techniques, including partial least squares (PLS), canonical correlation analysis (CCA) and reduced rank regression (RRR), into the area of Systems Biology. This new approach aims to extract the important proteins embedded in complex signal transduction pathway models.The analysis is performed on a model of intracellular signalling along the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in interleukin-6 (IL6) stimulated hepatocytes, which produce signal transducer and activator of transcription factor 3 (STAT3).A region of redundancy within the MAPK pathway that does not affect the STAT3 transcription was identified using CCA. This is the core finding of this analysis and cannot be obtained by inspecting the model by eye. In addition, RRR was found to isolate terms that do not significantly contribute to changes in protein concentrations, while the application of PLS does not provide such a detailed picture by virtue of its construction.This analysis has a similar objective to conventional model reduction techniques with the advantage of maintaining the meaning of the states prior to and after the reduction process. A significant model reduction is performed, with a marginal loss in accuracy, offering a more concise model while maintaining the main influencing factors on the STAT3 transcription.The findings offer a deeper understanding of the reaction terms involved, confirm the relevance of several proteins to the production of Acute Phase Proteins and complement existing findings regarding cross-talk between the two signalling pathways.

Performing meta-analysis with incomplete statistical information in clinical trials

Background: Results from clinical trials are usually summarized in the form of sampling distributions. When full information (mean, SEM) about these distributions is given, performing meta-analysis is straightforward. However, when some of the sampling distributions only have mean values, a challenging issue is to decide how to use such distributions in meta-analysis. Currently, the most common approaches are either ignoring such trials or for each trial with a missing SEM, finding a similar trial and taking its SEM value as the missing SEM. Both approaches have drawbacks. As an alternative, this paper develops and tests two new methods, the first being the prognostic method and the second being the interval method, to estimate any missing SEMs from a set of sampling distributions with full information. A merging method is also proposed to handle clinical trials with partial information to simulate meta-analysis.