Role of anatomical sites and correlated risk factors on the survival of orthodontic miniscrew implants:a systematic review and meta-analysis

Abstract Objectives The aim of this review was to systematically evaluate the failure rates of miniscrews related to their specific insertion site and explore the insertion site dependent risk factors contributing to their failure. Search methods An electronic search was conducted in the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Knowledge, Scopus, MEDLINE and PubMed up to October 2017. A comprehensive manual search was also performed. Eligibility criteria Randomised clinical trials and prospective non-randomised studies, reporting a minimum of 20 inserted miniscrews in a specific insertion site and reporting the miniscrews’ failure rate in that insertion site, were included. Data collection and analysis Study selection, data extraction and quality assessment were performed independently by two reviewers. Studies were sub-grouped according to the insertion site, and the failure rates for every individual insertion site were analysed using a random-effects model with corresponding 95% confidence interval. Sensitivity analyses were performed in order to test the robustness of the reported results. Results Overall, 61 studies were included in the quantitative synthesis. Palatal sites had failure rates of 1.3% (95% CI 0.3–6), 4.8% (95% CI 1.6–13.4) and 5.5% (95% CI 2.8–10.7) for the midpalatal, paramedian and parapalatal insertion sites, respectively. The failure rates for the maxillary buccal sites were 9.2% (95% CI 7.4–11.4), 9.7% (95% CI 5.1–17.6) and 16.4% (95% CI 4.9–42.5) for the interradicular miniscrews inserted between maxillary first molars and second premolars and between maxillary canines and lateral incisors, and those inserted in the zygomatic buttress respectively. The failure rates for the mandibular buccal insertion sites were 13.5% (95% CI 7.3–23.6) and 9.9% (95% CI 4.9–19.1) for the interradicular miniscrews inserted between mandibular first molars and second premolars and between mandibular canines and first premolars, respectively. The risk of failure increased when the miniscrews contacted the roots, with a risk ratio of 8.7 (95% CI 5.1–14.7). Conclusions Orthodontic miniscrew implants provide acceptable success rates that vary among the explored insertion sites. Very low to low quality of evidence suggests that miniscrews inserted in midpalatal locations have a failure rate of 1.3% and those inserted in the zygomatic buttress have a failure rate of 16.4%. Moderate quality of evidence indicates that root contact significantly contributes to the failure of interradicular miniscrews placed between the first molars and second premolars. Results should be interpreted with caution due to methodological drawbacks in some of the included studies

OBJECTIVE TRACK QUALITY INDICES

A set of new track quality indices (TQIs) has been developed to aid in the evaluation of track geometry measurements and to complement the well-established track classification based on the Federal Track Safety Standards (FTSS). The newly developed TQIs will be eventually implemented as real-time processed indicators on FRA\u27s research vehicle, the T-16. After a review of existing methods for determining TQIs, it is concluded that none of the current methods can satisfy the stated objectives. Therefore, a new methodology has been developed and tested to compute TQIs from track geometry data based on a simple and intuitive approach. The results have shown that the new TQIs can quantitatively describe the relative condition of track surface geometries. Furthermore, it is shown that the new TQIs are well correlated to the FTSS for each track class, providing a relative indication of quality within each class. A total of 48 track geometry surveys are selected from measurements taken by the FRA T-2000 track geometry car. Track geometry data from these surveys are processed and divided into 528-ft segments. For each track segment, the FTSS is used to determine actual track class. The track segments are grouped according to the actual classes these segments meet. Track quality indices are computed over each track segment. The results are used to establish TQI thresholds for each track class. The findings indicate that there are distinct TQI ranges for each track class, although there is some overlap between the upper boundary of one class and the lower boundary of the next

JRC2009-63045 A NONLINEAR RAIL VEHICLE DYNAMICS COMPUTER PROGRAM SAMS/RAIL PART 1: THEORY AND FORMULATIONS

ABSTRACT Accurate prediction of railroad vehicle performance requires detailed formulations of wheel-rail contact models. In the past, most dynamic simulation tools used an offline wheel-rail contact element based on look-up tables that are used by the main simulation solver. Nowadays, the use of an online nonlinear three-dimensional wheel-rail contact element is necessary in order to accurately predict the dynamic performance of high speed trains. Recently, the Federal Railroad Administration, Office of Research and Development has sponsored a project to develop a general multibody simulation code that uses an online nonlinear three-dimensiona

JRC2009-63046 A NONLINEAR RAIL VEHICLE DYNAMICS COMPUTER PROGRAM SAMS/RAIL PART 3: APPLICATIONS TO PREDICT RAILROAD VEHICLE-TRACK INTERACTION PERFORMANCE

ABSTRACT The dynamic response of a railroad vehicle traveling at speed over track deviations can be predicted by using multibody simulation codes. In this case, the solution of nonlinear equations of motion and extensive calculations based on the suspension characteristics of the vehicle are required. Recently, the Federal Railroad Administration, Office of Research and Development has sponsored a project to develop a general multibody simulation code that uses an online nonlinear threedimensional wheel-rail contact element to simulate contact forces between wheel and rail. In this paper, several applications to examine such issues as critical speed, curving performance at varying cant deficiencies, and wheel load equalization are presented to demonstrate the use of the multibody code. In addition, the application of the multibody code can be extended to train a neural network system. Neural network technology has the ability to learn relationships between a mechanical system input and output, and, once learned, give quick outputs for given input. The neural network can be combined with the use of a nonlinear multibody code to predict the performance of multiple railroad vehicle types in real time. In this paper, this system is briefly presented to shed light on the optimum use of the multibody code to prevent derailment