Draft programme of work of the Working Group on international classifications of the Statistical Conference of the Americas of the Economic Commission for Latin America and the Caribbean

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Findings of the survey on international classifications in Latin America

Spanish document available at the Library

Avance de la Cuenta Satélite de Servicios de Salud en la Región de las Américas

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Proposal for a code of good practice in statistics in Latin America and the Caribbean and implementation plan for 2012-2013

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Propuesta de código de buenas prácticas de las estadísticas de América Latina y el Caribe y plan de implementación para 2012-2013

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Resultados de la encuesta sobre clasificaciones internacionales en América Latina

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Propuesta del plan de trabajo 2012-2013 del Grupo de Trabajo sobre clasificaciones internacionales de la conferencia estadística de las Américas de la CEPAL

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Study to establish an evaluation system for State of Iowa Merit Employment System classifications on the basis of comparable worth, Statistical Supplement, 1984

This is the statistical supplement to the final report of the study to establish an evaluation system for State of Iowa Merit Employment System classifications on the basis of comparable worth.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Survival, classifications, and desmosomal plaque genes in non-small cell lung cancer.

Novel biomarkers are required to improve prognostic predictions obtained with lung cancer staging systems. This study of 62 surgically-treated Non-Small Cell Lung Cancer (NSCLC) patients had two objectives: i) to compare the predictive value of T-stage classifications between the 6(th) and 7(th) editions of the Tumor, Node, and Metastasis staging system (TNM); and ii) to examine the association of Pkp1 and/or Krt15 gene expression with survival and outcomes. Multivariate and Kaplan-Meier survival analyses were performed, examining the relationship of survival with T-stage, recurrence, and TNM-stage (by each TNM edition) and with the single/combined expression of Pkp1 and/or Krt15 genes. Five-year survival rates only significantly differed as a function of T-stage in patients without recurrence when estimated using the 6(th) edition of the TNM classification and only in patients in pathologic TNM-stage IA using the 7(th). Overall survival for patients with elevated expression of both genes was 13.5 months in those with adenocarcinoma and 34.6 months in those with squamous cell carcinoma. Overall survival was 30.4 months in patients with Pkp1 gene upregulation and 30.9 months in those with Krt15 gene upregulation. In conclusion, survival estimations as a function of T-staging differed between the 6(th) and 7(th) editions of TNM. Overall survival differed according to the expression of Pkp1 and/or Krt15 genes, although this relationship did not reach statistical significance.

Evaluation of the agreement by examiners according to classifications of third molars

Objectives. This study recorded and evaluated the intra-and inter-group agreement degree by different examiners for the classification of lower third molars according to both the Winter's and Pell & Gregory's systems. Study Design. An observational and cross-sectional study was realized with forty lower third molars analyzed from twenty digital panoramic radiographs. Four examiner groups (undergraduates, maxillofacial surgeons, oral radiologists and clinical dentists) from Aracaju, Sergipe, Brazil, classified them in relation to angulation, class and position. The variance test (ANOVA) was applied in the examiner findings with significance level of p<0.05 and confidence intervals of 95%. Results. Intra- and inter-group agreement was observed in Winter's classification system among all examiners. Pell & Gregory's classification system showed an average intra-group agreement and a statistical significant difference to position variable in inter-group analysis with greater disagreement to the clinical dentists group (p<0.05). Conclusions. High reproducibility was associated to Winter's classification, whereas the system proposed by Pell & Gregory did not demonstrate appropriate levels of reliability.

Impact Of Pharmacist Interventions On Drug-related Problems And Laboratory Markers In Outpatients With Human Immunodeficiency Virus Infection.

Substantial complexity has been introduced into treatment regimens for patients with human immunodeficiency virus (HIV) infection. Many drug-related problems (DRPs) are detected in these patients, such as low adherence, therapeutic inefficacy, and safety issues. We evaluated the impact of pharmacist interventions on CD4+ T-lymphocyte count, HIV viral load, and DRPs in patients with HIV infection. In this 18-month prospective controlled study, 90 outpatients were selected by convenience sampling from the Hospital Dia-University of Campinas Teaching Hospital (Brazil). Forty-five patients comprised the pharmacist intervention group and 45 the control group; all patients had HIV infection with or without acquired immunodeficiency syndrome. Pharmaceutical appointments were conducted based on the Pharmacotherapy Workup method, although DRPs and pharmacist intervention classifications were modified for applicability to institutional service limitations and research requirements. Pharmacist interventions were performed immediately after detection of DRPs. The main outcome measures were DRPs, CD4+ T-lymphocyte count, and HIV viral load. After pharmacist intervention, DRPs decreased from 5.2 (95% confidence interval [CI] =4.1-6.2) to 4.2 (95% CI =3.3-5.1) per patient (P=0.043). A total of 122 pharmacist interventions were proposed, with an average of 2.7 interventions per patient. All the pharmacist interventions were accepted by physicians, and among patients, the interventions were well accepted during the appointments, but compliance with the interventions was not measured. A statistically significant increase in CD4+ T-lymphocyte count in the intervention group was found (260.7 cells/mm(3) [95% CI =175.8-345.6] to 312.0 cells/mm(3) [95% CI =23.5-40.6], P=0.015), which was not observed in the control group. There was no statistical difference between the groups regarding HIV viral load. This study suggests that pharmacist interventions in patients with HIV infection can cause an increase in CD4+ T-lymphocyte counts and a decrease in DRPs, demonstrating the importance of an optimal pharmaceutical care plan.

Association between overbite and craniofacial growth pattern

The purpose of the present study was to assess the association between overbite and craniofacial growth pattern. The sample comprised eighty-six cephalograms obtained during the orthodontic pretreatment phase and analyzed using the Radiocef program to identify the craniofacial landmarks and perform orthodontic measurements. The variables utilized were overbite, the Jarabak percentage and the Vert index, as well as classifications resulting from the interpretation of these measurements. In all the statistical tests, a significance level of 5% was considered. Measurement reliability was checked by calculating method error. Weighted Kappa analysis showed that agreement between the facial types defined by the Vert index and the direction of growth trend established by the Jarabak percentage was not satisfactory. Owing to this lack of equivalency, a potential association between overbite and craniofacial growth pattern was evaluated using the chi-square test, considering the two methods separately. No relationship of dependence between overbite and craniofacial growth pattern was revealed by the results obtained. Therefore, it can be concluded that the classification of facial growth pattern will not be the same when considering the Jarabak and the Ricketts anayses, and that increased overbite cannot be associated with a braquifacial growth pattern, nor can openbite be associated with a dolichofacial growth pattern.