Introduction This retrospective cohort study compared clinical and radiographic outcomes of endodontic treatment performed in immature non-vital permanent teeth, by apexification (calcium hydroxide or apical barrier with Mineral Trioxide Aggregate (MTA)), versus revascularization. Methods A comprehensive chart review was performed to obtain a cohort of sequential previously completed cases with recalls. Clinical and radiographic data were collected for 31 treated teeth (19 revascularization and 12 apexification) with an average follow up time of 17 months and a recall rate of 63%. Tooth survival, success rate, and adverse events were analyzed. Changes in radiographic root length, width and area were quantified. Results The majority of treated teeth survived throughout the study period with 30/31 (97%) teeth surviving (18/19 (95%) revascularization, 12/12 apexification). Most cases were also clinically successful with 27/31 (87%) meeting criteria for success, (15/19 (78%) revascularization and 12/12 apexification; non-significant difference). A greater incidence of adverse events was observed in the revascularization group (8/19 (42%) versus 1/12 (11%) in apexification (Risk Ratio= 5.1, p=0.04, 95%CI (0.719, 35.48)). Although more revascularization cases than apexification cases demonstrated an increase in radiographic root area and width, the effect was not statistically significant. Conclusion In this study, revascularization was not superior to other apexification techniques in either clinical or radiographic outcomes. Studies with large subject cohorts, and long follow up periods are needed to evaluate outcomes of revascularization and apexification, while accounting for important co-variants relevant to clinical success.

In this article, we propose a new methodology based on a (log) semi-nonparametric (log-SNP) distribution that nests the lognormal and enables better fits in the upper tail of the distribution through the introduction of new parameters. We test the performance of the lognormal and log-SNP distributions capturing firm size, measured through a sample of US firms in [2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015]. Taking different levels of aggregation by type of economic activity, our study shows that the log-SNP provides a better fit of the firm size distribution. We also formally introduce the multivariate log-SNP distribution, which encompasses the multivariate lognormal, to analyze the estimation of the joint distribution of the value of the firm's assets and sales. The results suggest that sales are a better firm size measure, as indicated by other studies in the literature.

Research productivity distributions exhibit heavy tails because it is common for a few researchers to accumulate the majority of the top publications and their corresponding citations. Measurements of this productivity are very sensitive to the field being analyzed and the distribution used. In particular, distributions such as the lognormal distribution seem to systematically underestimate the productivity of the top researchers. In this article, we propose the use of a (log)semi-nonparametric distribution (log-SNP) that nests the lognormal and captures the heavy tail of the productivity distribution through the introduction of new parameters linked to high-order moments. To compare the results,

The spot price of electricity is highly skewed and heavy-tailed, as a result of the interaction of different variables that affect that market. Such characteristics impact the design of power plants with different technologies, fuel prices, and energy demand. This paper introduces the semi-nonparametric (SNP) approach to describe the uncertainty of different variables in an electricity market, reducing the limitations that normality and parametric density functions impose. The selection of probability density functions is achieved in terms of a finite Gram-Charlier expansion fitted by the maximum likelihood criterion. The study case is the Colombian electricity market, where the SNP distribution outperforms the normal distribution for spot price, national energy demand, the climate index ONI, and the series of hydrologic inflows of the system and some rivers. The results show that risk analysis in electricity markets requires the measurement of skewness, kurtosis, and high-order moments. The flexible methodology in our study has directly applications for implementing policies on electricity markets that improve the sustainability indicators of different systems. The particular characteristics of the series under analysis should be considered as a starting point for risk analysis and portfolio choice.

Research productivity distributions exhibit heavy tails because it is common for a few researchers to accumulate the majority of the top publications and their corresponding citations. Measurements of this productivity are very sensitive to the field being analyzed and the distribution used. In particular, distributions such as the lognormal distribution seem to systematically underestimate the productivity of the top researchers. In this article, we propose the use of a (log)semi-nonparametric distribution (log-SNP) that nests the lognormal and captures the heavy tail of the productivity distribution through the introduction of new parameters linked to high-order moments. To compare the results,