Problemas de produto cartesiano, raciocínio combinatório e intervenção do professor / Problems of cartesian product, combinatory reasoning, and teacher's intervention

O artigo descreve a natureza das soluções de crianças a problemas de produto cartesiano conforme níveis do raciocínio combinatório ali implicados, para identificar a aprendizagem ocorrente e a natureza das intervenções de ensino. Participaram cinco crianças de nove anos, alunas da 3ª série de uma escola municipal de Ensino Fundamental. A coleta de dados foi realizada em duas sessões individuais para solucionar problemas por escrito. A intervenção de ensino da pesquisadora seguiu o estilo clínico-crítico. Da análise qualitativa dos dados gravados em vídeo foram identificados os seguintes níveis de solução: resposta contextualizada sem indício de combinação, primeiras aproximações à solução combinatória, obtenção de algumas combinações e presença de solução combinatória. As formas identificadas de intervenção do adulto foram: orientadora, reorientadora, questionadora e instigadora. A discussão sublinha a relação das intervenções de ensino descritas na ocorrência dos progressos pontuais das crianças em direção a soluções de caráter combinatório.

This paper describes the nature of children's solutions to Cartesian product problems according to their implied levels of combinatory reasoning in order to identify the learning process going on therein, and the nature of teaching intervention. Five nine-year-old students enrolled at the 3rd grade of a state elementary school were the participants. Data were collected during two individual sessions of problem solving. The researcher's teaching intervention was on the clinical-critical format. The qualitative analysis of videotape data resulted in the identification of the following solution levels: contextualized answer without signs of combination, the first approaches to combinatory solutions, the arising of some combinations, and the presence of combinatory solutions. The identified forms of teaching interventions were: orienting, re-orienting, questioning, and instigating. The discussion underlies the relevance of the described teaching interventions to the punctual progress of each child towards solutions with combinatory marks.

Data Mining Technique for Signal Detection of Drug Adverse Events Using Health Insurance Claims / 薬剤疫学

Objective : To detect signals of potential drug adverse events (DAEs) through data mining of health insurance claims.<BR>Design and Data : Retrospective observational study. The data used were the database of health insurance claims collected and maintained by the Japan Medical Data Center consisting of 312, 797 medical and pharmaceutical claims in one year (August 2003 through July 2004) linked uniquely for 35, 410 patients using an encryption technique to ensure privacy.<BR>Methods : We counted all combinations (cross product or Cartesian product) of drugs and diagnoses appearing in the same claims and counted the number of times a given drug was prescribed preceding the suspected diagnosis in all combinations of the drug and the diagnosis appearing in a claim, i.e., the prescription date precedes the diagnosing date (the preceding number). We calculated the expected preceding number from the overall prevalence of drugs and diagnoses, and then calculated the observed and expected ratio, which was used as the signal indices. We calculated the signal indices on the health insurance claims data to detect DAEs of psychiatric drugs.<BR>Results : Amoxapine and trazodone HCL showed high signal indices with paralytic ileus and convulsion (epilepsy) as documented in their package inserts. However, paroxetine HCL and etizolam showed high signal indices with these potential adverse events although no such DAEs are documented in their package inserts.<BR>Conclusions : The undocumented high signal indices observed between the drugs and diagnoses indicate the potential DAEs and warrant in-depth pharmacovigilance. Given the strength of health insurance claims with a well-defined source population and accurate drug exposure, the proposed signal index will likely prove to be an effective data mining technique when combined with nested case-control analysis and counter-matching.