Evaluation of exposure index (IgM) in orthopaedic radiography

The exposure index (lgM) obtained from a radiographic image may be a useful feedback indicator to the radiographer about the appropriate exposure level in routine clinical practice. This study aims to evaluate lgM in orthopaedic radiography performed in the standard clinical environment. We analysed the lgM of 267 exposures performed with an AGFA CR system. The mean value of lgM in our sample is 2.14. A significant difference (P=0.000<0.05) from 1.96 lgM reference is shown. Data show that 72% of exposures are above the 1.96 lgM and 42% are above the limit of 2.26. Median values of lgM are above 1.96 and below 2.26 for Speed class (SC) 200 (2.16) and SC400 (2.13). The interquartile range is lower in SC400 than in SC200. Data seem to indicate that lgM values are above the manufacturer’s reference of 1.96. Departmental exposure charts should be optimised to reduce the dose given to patients.

Volatility regimes for the VIX index

This article presents a Markov chain framework to characterize the behavior of the CBOE Volatility Index (VIX index). Two possible regimes are considered: high volatility and low volatility. The specification accounts for deviations from normality and the existence of persistence in the evolution of the VIX index. Since the time evolution of the VIX index seems to indicate that its conditional variance is not constant over time, I consider two different versions of the model. In the first one, the variance of the index is a function of the volatility regime, whereas the second version includes an autoregressive conditional heteroskedasticity (ARCH) specification for the conditional variance of the index.

Comparing clustering solutions: the use of adjusted paired indices

In the present paper we compare clustering solutions using indices of paired agreement. We propose a new method - IADJUST - to correct indices of paired agreement, excluding agreement by chance. This new method overcomes previous limitations known in the literature as it permits the correction of any index. We illustrate its use in external clustering validation, to measure the accordance between clusters and an a priori known structure. The adjusted indices are intended to provide a realistic measure of clustering performance that excludes agreement by chance with ground truth. We use simulated data sets, under a range of scenarios - considering diverse numbers of clusters, clusters overlaps and balances - to discuss the pertinence and the precision of our proposal. Precision is established based on comparisons with the analytical approach for correction specific indices that can be corrected in this way are used for this purpose. The pertinence of the proposed correction is discussed when making a detailed comparison between the performance of two classical clustering approaches, namely Expectation-Maximization (EM) and K-Means (KM) algorithms. Eight indices of paired agreement are studied and new corrected indices are obtained.