Educational professionals’ understanding of childhood traumatic brain injury

Primary objectives: To determine the understanding of educational professionals around the topic of childhood brain injury and explore the factor structure of the Common Misconceptions about Traumatic Brain Injury Questionnaire (CM-TBI).

Research design: Cross sectional postal survey.

Methods and procedures: The CM-TBI was posted to all educational establishments in one region of the United Kingdom. One representative from each school was asked to complete and return the questionnaire (N = 388).

Main outcomes and results: Differences were demonstrated between those participants who knew someone with a brain injury and those who did not, with a similar pattern being shown for those educators who had taught a child with brain injury. Participants who had taught a child with brain injury demonstrated greater knowledge in areas such as seatbelts/prevention, brain damage, brain injury sequelae, amnesia, recovery, and rehabilitation. Principal components analysis suggested the existence of four factors and the discarding of half the original items of the questionnaire.

Conclusions: In the first European study to explore this issue, we highlight that teachers are ill prepared to cope with children who have sustained a brain injury. Given the importance of a supportive school environment in return to life following hospitalisation, the lack of understanding demonstrated by teachers in this research may significantly impact on a successful return to school.

Effect of two different rice dehusking procedures on total arsenic concentration in rice

Pollution of subterranean water by arsenic (As) in Asia has resulted in the worst chemical disaster in human history. For populations living on subsistence rice diets, As contamination of rice grain contributes greatly to dietary As exposure. The main objectives of this study were to compare two dehusking processes: (a) wet process (soaking of rice, boiling and mechanical hulling) and (b) dry process (mechanical hulling), and recommend the method leading to a lower As content in commercial rice. In general, hulling of paddy rice (373 mu g As kg(-1)) significantly decreased As content in rice grain (311 mu g As kg(-1)). The final As concentrations in boiled rice (final product of the wet process) and atab rice (dry process) were 332 and 290 mu g kg(-1). Thus, the dry method is recommended for dehusking paddy rice if not As-free water is available. However, villagers can reduce the As content in the wet system by discarding the soaking water and using new water for the light boiling. Finally, it is not recommended to use rice husk for feeding animals because the As concentration is very high, approximately 1,000 mu g As kg(-1).

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).