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Objectives: The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT). Material and methods: Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting. Results: Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77. Conclusion: This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value. Clinical relevance: In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.
Objectives: The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT). Material and methods: Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting. Results: Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77. Conclusion: This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value. Clinical relevance: In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.
Background: More than 170 species of tabanids are known in Europe, with many occurring only in limited areas or having become very rare in the last decades. They continue to spread various diseases in animals and are responsible for livestock losses in developing countries. The current monitoring and recording of horseflies is mainly conducted throughout central Europe, with varying degrees of frequency depending on the country. To the detriment of tabanid research, little cooperation exists between western European and Eurasian countries.
Methods: For these reasons, we have compiled available sources in order to generate as complete a dataset as possible of six horsefly species common in Europe. We chose Haematopota pluvialis, Chrysops relictus, C. caecutiens, Tabanus bromius, T. bovinus and T. sudeticus as ubiquitous and abundant species within Europe. The aim of this study is to estimate the distribution, land cover usage and niches of these species. We used a surface-range envelope (SRE) model in accordance with our hypothesis of an underestimated distribution based on Eurocentric monitoring regimes.
Results: Our results show that all six species have a wide range in Eurasia, have a broad climatic niche and can therefore be considered as widespread generalists. Areas with modelled habitat suitability cover the observed distribution and go far beyond these. This supports our assumption that the current state of tabanid monitoring and the recorded distribution significantly underestimates the actual distribution. Our results show that the species can withstand extreme weather and climatic conditions and can be found in areas with only a few frost-free months per year. Additionally, our results reveal that species prefer certain land-cover environments and avoid other land-cover types.
Conclusions: The SRE model is an effective tool to calculate the distribution of species that are well monitored in some areas but poorly in others. Our results support the hypothesis that the available distribution data underestimate the actual distribution of the surveyed species.
Quantitative models have several advantages compared to qualitative methods for pest risk assessments (PRA). Quantitative models do not require the definition of categorical ratings and can be used to compute numerical probabilities of entry and establishment, and to quantify spread and impact. These models are powerful tools, but they include several sources of uncertainty that need to be taken into account by risk assessors and communicated to decision makers. Uncertainty analysis (UA) and sensitivity analysis (SA) are useful for analyzing uncertainty in models used in PRA, and are becoming more popular. However, these techniques should be applied with caution because several factors may influence their results. In this paper, a brief overview of methods of UA and SA are given. As well, a series of practical rules are defined that can be followed by risk assessors to improve the reliability of UA and SA results. These rules are illustrated in a case study based on the infection model of Magarey et al. (2005) where the results of UA and SA are shown to be highly dependent on the assumptions made on the probability distribution of the model inputs.