Understanding prehospital blood transfusion decision-making for injured patients : an interview study

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.Peer reviewe

Taenia solium Infections in a Rural Area of Eastern Zambia-A Community Based Study

Taenia solium taeniosis/cysticercosis is a zoonotic infection endemic in many developing countries, with humans as the definitive host (taeniosis) and pigs and humans as the intermediate hosts (cysticercosis). When humans act as the intermediate host, the result can be neurocysticercosis, which is associated with acquired epilepsy, considerable morbidity and even mortality. In Africa, most studies have been carried out in pigs with little or no data in humans available. In this human study, conducted in a rural community in Eastern Zambia, prevalences for taeniosis and cysticercosis were determined at 6.3% and 5.8% respectively, indicating the hyperendemicity of the area. Cysticercosis infection was strongly related with age, with a significant increase in prevalence occurring in individuals from the age of 30 onward. A collected tapeworm was confirmed to be T. solium. Risk factors associated with the transmission and maintenance of the parasite such as free roaming pigs, households without latrines, backyard slaughter of pigs without inspection and consumption of undercooked pork were also present. The findings of this work have identified the need for further research in the transmission dynamics and the burden that this infection has on the resources of poor local people

Animal influence on water, sanitation and hygiene measures for zoonosis control at the household level: A systematic literature review

Neglected zoonotic diseases (NZDs) have a significant impact on the livelihoods of the world’s poorest populations, which often lack access to basic services. Water, sanitation and hygiene (WASH) programmes are included among the key strategies for achieving the World Health Organization’s 2020 Roadmap for Implementation for control of Neglected Tropical Diseases (NTDs). There exists a lack of knowledge regarding the effect of animals on the effectiveness of WASH measures. This review looked to identify how animal presence in the household influences the effectiveness of water, hygiene and sanitation measures for zoonotic disease control in low and middle income countries; to identify gaps of knowledge regarding this topic based on the amount and type of studies looking at this particular interaction

A Process for Evaluating Explanations for Transparent and Trustworthy AI Prediction Models

This study proposes a process to generate and validate algorithmic explanations for the reasoning of an AI prediction model, implemented using a Bayesian network (BN). The intention of the generated explanations is to increase the transparency and trustworthiness of a decision-support system that uses a BN prediction model. To achieve this, explanations should be presented in an easy-to-understand, clear, and concise natural language narrative. We have developed an algorithm for explaining the reasoning of a prediction made using a BN. For the narrative part of the explanation, we use a template which presents the 'content' part of the explanation; this content is a word-less information structure that applies to all BNs. The template, on the other hand, needs to be designed specifically for each BN model. In this paper, we use a BN for the risk of trauma-induced coagulopathy, a critical bleeding problem. We outline a process for using experts' explanations as the basis for designing the explanation template. We do not believe that an algorithmic explanation needs to be indistinguishable from expert explanations; instead we aim to imitate the narrative structure of explanations given by experts, although we find that there is considerable variation in these. We then consider how the generated explanations can be evaluated, since a direct comparison (in the style of a Turing test) would likely fail. We describe a study using questionnaires and interviews to evaluate the effect of an algorithmic explanation on the transparency and also on the trustworthiness of the predictions made by the system. The preliminary results of our study suggest that the presence of an explanation makes the AI model more transparent but not necessarily more trustworthy