Prosthetic misfit of implant-supported prosthesis obtained by an alternative section method

PURPOSE. Adequate passive-fitting of one-piece cast 3-element implant-supported frameworks is hard to achieve. This short communication aims to present an alternative method for section of one-piece cast frameworks and for casting implant-supported frameworks. MATERIALS AND METHODS. Three-unit implant-supported nickel-chromium (Ni-Cr) frameworks were tested for vertical misfit (n = 6). The frameworks were cast as one-piece (Group A) and later transversally sectioned through a diagonal axis (Group B) and compared to frameworks that were cast diagonally separated (Group C). All separated frameworks were laser welded. Only one side of the frameworks was screwed. RESULTS. The results on the tightened side were significantly lower in Group C (6.43 +/- 3.24 mu m) when compared to Groups A (16.50 +/- 7.55 mu m) and B (16.27 +/- 1.71 mu m) (P<.05). On the opposite side, the diagonal section of the one-piece castings for laser welding showed significant improvement in the levels of misfit of the frameworks (Group A, 58.66 +/- 14.30 mu m; Group B, 39.4.8 +/- 12.03 mu m; Group C, 23.13 +/- 8.24 mu m) (P<.05). CONCLUSION. Casting diagonally sectioned frameworks lowers the misfit levels. Lower misfit levels for the frameworks can be achieved by diagonally sectioning one-piece frameworks. [J Adv Prosthodont 2012;4:89-92]

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.