Non-nutritive sucking scoring system for preterm newborns

Aim: To validate a non-nutritive sucking (NNS) scoring system for oral feeding in preterm newborns (PTNB). Methods: A cohort study was carried out in two phases. In phase one of the study, 22 mastered speech-language pathologists received the protocol and procedure for a NNS scoring system to evaluate the content and presentation of the form and to define the grading scale. In phase two, six speech-language pathologists evaluated 51 PTNBs weekly, using the defined scoring system. Setting: This study was carried out in the Nursery Annex to the Maternity at the Intensive and Neonatal Pediatrics Service, Instituto da Crianca, Hospital das Clinicas, School of Medicine, University of Sao Paulo (FMUSP) during the period from May 2004 to May 2006. Participants: A total of 28 speech-language pathologist experts and 51 PTNBs. Results: In the first phase of the study, 22 speech-language pathologists selected the criteria, utilized in the NNS evaluation with 80% agreement. In the second phase of the study, the NNS evaluation was carried out on 51 PTNB, and a scoring system of 50 points was proposed, which corresponds to the smallest number of false positive and negative results regarding oral feeding ability. Conclusion: An NNS evaluation system was validated that was able to indicate when oral feeding could safely begin in PTNBs with a high level of agreement among the speech-language pathologists who have participated.

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. (C) 2011 Elsevier Ltd. All rights reserved.