Fracture of costochondral graft in temporomandibular joint reconstructive surgery: an unexpected complication

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Ectodermale dysplasie : een heterogene afwijking

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Epidemiologie van het aangezichtsletsel

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Variant of odontoonychodermal dysplasia?

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Autogenous bone and bovine bone mineral as grafting materials in maxillofacial surgery

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Effectiveness of primary correction of traumatic telecanthus

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Tooth eruption through autogenous and xenogenous bone transplants : a histological and radiographic evaluation in beagle dogs

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Een preauriculaire zwelling: niet altijd de parotis

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Epidemiologie van het aangezichtsletsel

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications

[Use of root-end filling materials in a surgical apical endodontic treatment in the Netherlands]

Contains fulltext : 69206.pdf (publisher's version ) (Open Access)The material to be used for closing the root canal when carrying out a retrograde surgical apical endodontic treatment continues to be a subject of discussion. The aim of the present study was to inventory which materials are being used for this purpose at this moment by Dutch O&MF surgeons. All practicing Dutch OM&F surgeons (n = 195) were sent a questionnaire. The response rate was 77%. The results showed that at this moment intermediate restoration material (IRM) is the retrograde filling material most widely used by the Dutch O&MF surgeons (47.6%) in cases of surgical apical endodontic treatment. Amalgam, with 35%, was second, especially due to its plasticity and convenience. The choice of material is just as often determined by tradition, personal preference, individual experience or scientific results. There seems to be no relationship between the dental surgeon's number of years of experience and the type of retro grade filling material which he or she uses