4 resultados para REGRESSION TREE

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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Background: Consensus development techniques were used in the late 1980s to create explicit criteria for the appropriateness of cataract extraction. We developed a new appropriateness of indications tool for cataract following the RAND method. We tested the validity of our panel results. Methods: Criteria were developed using a modified Delphi panel judgment process. A panel of 12 ophthalmologists was assembled. Ratings were analyzed regarding the level of agreement among panelists. We studied the influence of all variables on the final panel score using linear and logistic regression models. The explicit criteria developed were summarized by classification and regression tree analysis. Results: Of the 765 indications evaluated by the main panel in the second round, 32.9% were found appropriate, 30.1% uncertain, and 37% inappropriate. Agreement was found in 53% of the indications and disagreement in 0.9%. Seven variables were considered to create the indications and divided into three groups: simple cataract, with diabetic retinopathy, or with other ocular pathologies. The preoperative visual acuity in the cataractous eye and visual function were the variables that best explained the panel scoring. The panel results were synthesized and presented in three decision trees. Misclassification error in the decision trees, as compared with the panel original criteria, was 5.3%. Conclusion: The parameters tested showed acceptable validity for an evaluation tool. These results support the use of this indication algorithm as a screening tool for assessing the appropriateness of cataract extraction in field studies and for the development of practice guidelines.

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Background: Intratumor heterogeneity may be responsible of the unpredictable aggressive clinical behavior that some clear cell renal cell carcinomas display. This clinical uncertainty may be caused by insufficient sampling, leaving out of histological analysis foci of high grade tumor areas. Although molecular approaches are providing important information on renal intratumor heterogeneity, a focus on this topic from the practicing pathologist' perspective is still pending. Methods: Four distant tumor areas of 40 organ-confined clear cell renal cell carcinomas were selected for histopathological and immunohistochemical evaluation. Tumor size, cell type (clear/granular), Fuhrman's grade, Staging, as well as immunostaining with Snail, ZEB1, Twist, Vimentin, E-cadherin, beta-catenin, PTEN, p-Akt, p110 alpha, and SETD2, were analyzed for intratumor heterogeneity using a classification and regression tree algorithm. Results: Cell type and Fuhrman's grade were heterogeneous in 12.5 and 60 % of the tumors, respectively. If cell type was homogeneous (clear cell) then the tumors were low-grade in 88.57 % of cases. Immunostaining heterogeneity was significant in the series and oscillated between 15 % for p110a and 80 % for Snail. When Snail immunostaining was homogeneous the tumor was histologically homogeneous in 100 % of cases. If Snail was heterogeneous, the tumor was heterogeneous in 75 % of the cases. Average tumor diameter was 4.3 cm. Tumors larger than 3.7 cm were heterogeneous for Vimentin immunostaining in 72.5 % of cases. Tumors displaying negative immunostaining for both ZEB1 and Twist were low grade in 100 % of the cases. Conclusions: Intratumor heterogeneity is a common event in clear cell renal cell carcinoma, which can be monitored by immunohistochemistry in routine practice. Snail seems to be particularly useful in the identification of intratumor heterogeneity. The suitability of current sampling protocols in renal cancer is discussed.

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In this paper, reanalysis fields from the ECMWF have been statistically downscaled to predict from large-scale atmospheric fields, surface moisture flux and daily precipitation at two observatories (Zaragoza and Tortosa, Ebro Valley, Spain) during the 1961-2001 period. Three types of downscaling models have been built: (i) analogues, (ii) analogues followed by random forests and (iii) analogues followed by multiple linear regression. The inputs consist of data (predictor fields) taken from the ERA-40 reanalysis. The predicted fields are precipitation and surface moisture flux as measured at the two observatories. With the aim to reduce the dimensionality of the problem, the ERA-40 fields have been decomposed using empirical orthogonal functions. Available daily data has been divided into two parts: a training period used to find a group of about 300 analogues to build the downscaling model (1961-1996) and a test period (19972001), where models' performance has been assessed using independent data. In the case of surface moisture flux, the models based on analogues followed by random forests do not clearly outperform those built on analogues plus multiple linear regression, while simple averages calculated from the nearest analogues found in the training period, yielded only slightly worse results. In the case of precipitation, the three types of model performed equally. These results suggest that most of the models' downscaling capabilities can be attributed to the analogues-calculation stage.

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In this work I present recent scientific papers related to the concept of tree-depth: different characterizations, a game theoretic approach to it and recently discovered applications. The focus in this work is presenting all the ideas in a self-contained way, such that they can be easily understood with little previous knowledge. Apart from that all the ideas are presented in a homogeneous way with clear examples and all the lemmas, some of which didn’t have proofs in the papers, are presented with rigorous proofs.