Height and weight gains in a nutrition rehabilitation day-care service

Objective: To evaluate nutritional recovery patterns in 106 undernourished children assisted by the Center of Nutritional Recovery and Education (CREN, in Portuguese) between January 1995 and December 1999. Design: CREN assists undernourished children aged 0 to 72 months living in the southern regions of Sao Paulo, in an outpatient setting. Nutritional status was assessed by Z-scores of weight-for-age, height-for-age and weight-for-height. Nutritional recovery evaluation considered Z-score gains in weight-for-age and height-for-age, grouping into four categories (Z-score increment of 0.50 between groups). Children with birth weight less than 2500 g were classified as low birth weight (LBW), while those born at term and with LBW were classified as small for gestational age. Setting: CREN (Center of Nutritional Recovery and Education in Portuguese), Sao Paulo, Brazil. Subjects: One hundred and six children from CREN. Results: Among the 106 evaluated children, ninety-eight (92.5%)recovered their weight or height and seventy-two (67.9%) recovered both. Nearly half of studied children presented a nutritional recovery (increase in Z-score) of more than 0.50 in height-for-age (46.2%) and about 40% in weight-for-age (38.7%). Multivariate analysis showed that treatment duration and initial weight-for-age contributed to weight-for-age Z-score increment, explaining 25% of the variation; and treatment duration, initial height-for-age and weight-for-age Z-score increment contributed to height-for-age Z-score increment, explaining 62% of the variation. Conclusions: Our findings show that nutritional recovery among children who attended CREN was influenced primarily by the degree of nutritional deficit at admission. It has also been shown that biological variables are more important than socio-economic status in determining the rate of nutritional recovery.

Stroke awareness in Brazil - Alarming results in a community-based study

Background and Purpose-Stroke is the leading cause of death in Brazil. This community-based study assessed lay knowledge about stroke recognition and treatment and risk factors for cerebrovascular diseases and activation of emergency medical services in Brazil. Methods-The study was conducted between July 2004 and December 2005. Subjects were selected from the urban population in transit about public places of 4 major Brazilian cities: S (a) over tildeo Paulo, Salvador, Fortaleza, and Ribeir (a) over tildeo Preto. Trained medical students, residents, and neurologists interviewed subjects using a structured, open-ended questionnaire in Portuguese based on a case presentation of a typical patient with acute stroke at home. Results-Eight hundred fourteen subjects were interviewed during the study period (53.9% women; mean age, 39.2 years; age range, 18 to 80 years). There were 28 different Portuguese terms to name stroke. Twenty-two percent did not recognize any warning signs of stroke. Only 34.6% of subjects answered the correct nationwide emergency telephone number in Brazil (# 192). Only 51.4% of subjects would call emergency medical services for a relative with symptoms of stroke. In a multivariate analysis, individuals with higher education called emergency medical services (P=0.038, OR=1.5, 95%, CI: 1.02 to 2.2) and knew at least one risk factor for stroke (P<0.05, OR=2.0, 95% CI: 1.2 to 3.2) more often than those with lower education. Conclusions-Our study discloses alarming lack of knowledge about activation of emergency medical services and availability of acute stroke treatment in Brazil. These findings have implications for public health initiatives in the treatment of stroke and other cardiovascular emergencies.

An investigation into feature construction to assist word sense disambiguation

Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.