Success rate and efficiency of activator treatment

In a retrospective multicentre study, the success rate and efficiency of activator treatment were analysed. All patients from two University clinics (Giessen, Germany and Berne, Switzerland) that fulfilled the selection criteria (Class II division 1 malocclusion, activator treatment, no aplasia, no extraction of permanent teeth, no syndromes, no previous orthodontic treatment except transverse maxillary expansion, full available records) were included in the study. The subject material amounted to 222 patients with a mean age of 10.6 years. Patient records, lateral head films, and dental casts were evaluated. Treatment was classified as successful if the molar relationship improved by at least half to three-fourths cusp width depending on whether or not the leeway space was used during treatment. Group comparisons were carried out using Wilcoxon two-sample and Kruskal-Wallis tests. For discrete data, chi-square analysis was used and Fisher's exact test when the sample size was small. Stepwise logistic regression was also employed. The success rate was 64 per cent in Giessen and 66 per cent in Berne. The only factor that significantly (P < 0.001) influenced treatment success was the level of co-operation. In approximately 27 per cent of the patients at both centres, the post-treatment occlusion was an 'ideal' Class I. In an additional 38 per cent of the patients, marked improvements in occlusal relationships were found. In subjects with Class II division 1 malocclusions, in which orthodontic treatment is performed by means of activators, a marked improvement of the Class II dental arch relationships can be expected in approximately 65 per cent of subjects. Activator treatment is more efficient in the late than in the early mixed dentition.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Modeling absolute differences in life expectancy with a censored skew-normal regression approach.

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest.