Aim: We sought to determine whether there were differences between men and women with acute stroke in their baseline characteristics and outcome in a large cohort of patients randomized in the International Stroke Trial (IST). Methods: Of the 19,435 patients randomized in the IST, 17,370 had an ischemic stroke confirmed by CT scan or autopsy (8,003 female and 9,367 male). In males and females, we compared baseline characteristics (age, frequency of atrial fibrillation, pre-stroke administration of aspirin and systolic blood pressure, conscious level, stroke syndrome) and outcome at 14 days and 6 months (death, complications, dependency, recovery, place of residence). We developed a specific logistic regression model to adjust for case-mix in order to evaluate the separate influence of gender on outcome. Results: Female patients were older, suffered more frequently from atrial fibrillation, had higher systolic blood pressure at randomization and generally had more severe strokes (a higher proportion were unconscious or drowsy or had a total anterior circulation syndrome). Females had higher 14-day and 6-month case fatality and were more likely to be dead or dependent at six months (and consequently more likely to require institutional or residential care). Gender was an independent predictor of death or dependency at 6 months. Conclusions: The adverse effect of female gender on outcome indicates that further research to explore the underlying biological mechanism is justified, and that more intensive acute and long-term treatment may be needed to improve outcome among female patients with stroke.

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Despite the fact that many important problems (including clustering) can be described using hypergraphs, theoretical foundations as well as practical algorithms using hypergraphs are not well developed yet. In this paper, we propose a hypergraph modularity function that generalizes its well established and widely used graph counterpart measure of how clustered a network is. In order to define it properly, we generalize the Chung-Lu model for graphs to hypergraphs. We then provide the theoretical foundations to search for an optimal solution with respect to our hypergraph modularity function. A simple heuristic algorithm is described and applied to a few illustrative examples. We show that using a strict version of our proposed modularity function often leads to a solution where a smaller number of hyperedges get cut as compared to optimizing modularity of 2-section graph of a hypergraph.

Lower estimated insulin sensitivity was associated with risk for hyperfiltration over time, whereas increased albumin excretion was associated with hyperglycemia in youth-onset T2DM.