Objective To develop a causal model for the occurrence of neurocysticercosis (NC)‐related seizures and test hypotheses generated from the model. Methods We used data from a randomized controlled trial comparing albendazole with placebo among patients newly diagnosed with NC. Based on our causal model, we explored the associations among albendazole treatment, NC cyst evolution, and seizure outcomes over 24 months of follow‐up using generalized linear mixed effect models. Results We included 153 participants, of whom 51% received albendazole. The association between seizure outcomes and treatment over time demonstrated lack of linearity and heterogeneity, requiring the inclusion of time‐treatment interaction terms for valid modeling. Participants in the albendazole group had fewer seizures overall and of partial onset at all time points compared with the placebo group, but the difference increased over the first few months following treatment, then decreased over time. Generalized seizures exhibited a more complex association; those in the albendazole group had fewer seizures compared with those in the placebo group for the first few months after treatment, and then the association reversed and those in the placebo arm had fewer seizures. Adjusting for the number of NC cysts in each phase resulted in an attenuation of the strength of association between albendazole and seizure outcomes, consistent with mediation. Among participants in whom all cysts had disappeared (n = 21), none continued to have seizures. Significance Albendazole treatment is associated with a possible reduction in focal seizures in the short term (3‐6 months), perhaps by hastening the resolution of the cysts. However, the effect is not discernible over the long term, because most cysts either calcify or resolve completely, regardless of whether treated with albendazole. The stage of evolution of the cysticercus is an important consideration in the evaluation of albendazole effect on seizure outcome.

In this proposal, a real time bias estimation system for an airport surveillance data fusion system is presented. This bias estimation system is divided in two main parts. The first part estimates SMR bias terms, taking advantage of the knowledge of the airport map, which is useful because aircraft usually follow the axis of airport taxiways. The other part makes use of SMR corrected measures, which can be assumed to be unbiased. Using them, bias estimators for other important surface surveillance sensors are defined. These estimators are based on processing differences of measurement taken from each sensor and from the SMR. As simulation results show, if the sensor error models are precise enough, both estimations converge to the real bias values, and therefore unbiased measures may be obtained. These unbiased measurements should be provided to the fusion system, in order to enhance tracking performance. These estimation processes do not represent an important computer load increase for the data fusion system. The performance improvement in tracking is also presented.

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