One life ends, another begins: Management of a brain-dead pregnant mother - A systematic review -

Background: An accident or a catastrophic disease may occasionally lead to brain death (BD) during pregnancy. Management of brain-dead pregnant patients needs to follow special strategies to support the mother in a way that she can deliver a viable and healthy child and, whenever possible, also be an organ donor. This review discusses the management of brain-dead mothers and gives an overview of recommendations concerning the organ supporting therapy. Methods: To obtain information on brain-dead pregnant women, we performed a systematic review of Medline, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL). The collected data included the age of the mother, the cause of brain death, maternal medical complications, gestational age at BD, duration of extended life support, gestational age at delivery, indication of delivery, neonatal outcome, organ donation of the mothers and patient and graft outcome. Results: In our search of the literature, we found 30 cases reported between1982 and 2010. A nontraumatic brain injury was the cause of BD in 26 of 30 mothers. The maternal mean age at the time of BD was 26.5 years. The mean gestational age at the time of BD and the mean gestational age at delivery were 22 and 29.5 weeks, respectively. Twelve viable infants were born and survived the neonatal period. Conclusion: The management of a brain-dead pregnant woman requires a multidisciplinary team which should follow available standards, guidelines and recommendations both for a nontraumatic therapy of the fetus and for an organ-preserving treatment of the potential donor

Multidimensional IRT models to analyze learning outcomes of Italian students at the end of lower secondary school

In this paper, different multidimensional IRT models are compared in order to choose the best approach to explain response data on Italian student assessment at the end of lower secondary school. The results show that the additive model with three specific dimensions (reading comprehension, grammar, and mathematics abilities) and an overall ability is able to recover the test structure meaningfully. In this model, the overall ability compensates for the specific ability (or vice versa) in order to determine the probability of a correct response. Given the item characteristics, the overall ability is interpreted as a reasoning and thinking capability. Model estimation is conducted via Gibbs sampler within a Bayesian approach, which allows the use of Bayesian model comparison techniques such as posterior predictive model checking for model comparison and fit