Fracture Rates and Lifetime Estimations of CAD/CAM All-ceramic Restorations

The gathering of clinical data on fractures of dental restorations through prospective clinical trials is a labor- and time-consuming enterprise. Here, we propose an unconventional approach for collecting large datasets, from which clinical information on indirect restorations can be retrospectively analyzed. The authors accessed the database of an industry-scale machining center in Germany and obtained information on 34,911 computer-aided design (CAD)/computer-aided manufacturing (CAM) all-ceramic posterior restorations. The fractures of bridges, crowns, onlays, and inlays fabricated from different all-ceramic systems over a period of 3.5 y were reported by dentists and entered in the database. Survival analyses and estimations of future life revealed differences in performance among ZrO2-based restorations and lithium disilicate and leucite-reinforced glass-ceramics.

The importance of knowing when to stop. A sequential stopping rule for component-wise gradient boosting

OBJECTIVES

Component-wise boosting algorithms have evolved into a popular estimation scheme in biomedical regression settings. The iteration number of these algorithms is the most important tuning parameter to optimize their performance. To date, no fully automated strategy for determining the optimal stopping iteration of boosting algorithms has been proposed.

METHODS

We propose a fully data-driven sequential stopping rule for boosting algorithms. It combines resampling methods with a modified version of an earlier stopping approach that depends on AIC-based information criteria. The new "subsampling after AIC" stopping rule is applied to component-wise gradient boosting algorithms.

RESULTS

The newly developed sequential stopping rule outperformed earlier approaches if applied to both simulated and real data. Specifically, it improved purely AIC-based methods when used for the microarray-based prediction of the recurrence of metastases for stage II colon cancer patients.

CONCLUSIONS

The proposed sequential stopping rule for boosting algorithms can help to identify the optimal stopping iteration already during the fitting process of the algorithm, at least for the most common loss functions.