SummaryA questionnaire, designed to assess bleeding/bruising tendencies, was administered to 251 otherwise healthy children undergoing a tonsillectomy and/or adenoidectomy. 23 children with excessive bleeding during or after the operation, with a long bleeding time or who reported taking aspirin recently were excluded, to give a population of 228 non-bleeders. For comparative purposes, 31 patients with bleeding disorders (von Wille-brand’s disease and/or platelet function defects) were studied. A considerable proportion of “non-bleeding” children reported easy bruising (24%), had bruises at least once a week (36%) and suffered from nosebleeds (39%). The respective frequencies (67%, 68% and 69%) for children with bleeding disorders were significantly higher. Occurrence of bruises usually on more than one part of the body, frequent large bruises or hematomas were rare in “non-bleeders” (4.9%, 3.5% and 2.7% respectively), but more common in “bleeders” (38.5%, 29.6% and 21.7% respectively).

We study how classification accuracy can be improved when both different data preprocessing methods and computerized consensus diagnosis (CCD) are applied to 1H magnetic resonance (MR) spectra of astrocytomas, meningiomas, and epileptic brain tissue. The MR spectra (360 MHz, 37 degrees C) of tissue specimens (biopsies) from subjects with meningiomas (95; 26 cases), astrocytomas (74; 26 cases), and epilepsy (37; 8 cases) were preprocessed by several methods. Each data set was partitioned into training and validation sets. Robust classification was carried out via linear discriminant analysis (LDA), artificial neural nets (NN), and CCD, and the results were compared with histopathological diagnosis of the MR specimens. Normalization of the relevant spectral regions affects classification accuracy significantly. The spectra-based average three-class classification accuracies of LDA and NN increased from 81.7% (unnormalized data sets) to 89.9% (normalized). CCD increased the classification accuracy of the normalized sets to an average of 91.8%. CCD invariably decreases the fraction of unclassifiable spectra. The same trends prevail, with improved results, for case-based classification. Preprocessing the 1H MR spectra is essential for accurate and reliable classification of astrocytomas, meningiomas, and nontumorous epileptic brain tissue. CCD improves classification accuracy, with an attendant decrease in the fraction of unclassifiable spectra or cases.

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Background  Given the enormity of challenges involved in pandemic preparedness, design and implementation of effective and cost‐effective public health policies is a major task that requires an integrated approach through engagement of scientific, administrative, and political communities across disciplines. There is ample evidence to suggest that modeling may be a viable approach to accomplish this task. Methods  To demonstrate the importance of synergism between modelers, public health experts, and policymakers, the University of Winnipeg organized an interdisciplinary workshop on the role of models in pandemic preparedness in September 2008. The workshop provided an excellent opportunity to present outcomes of recent scientific investigations that thoroughly evaluate the merits of preventive, therapeutic, and social distancing mechanisms, where community structures, priority groups, healthcare providers, and responders to emergency situations are given specific consideration. Results  This interactive workshop was clearly successful in strengthening ties between various disciplines and creating venues for modelers to effectively communicate with policymakers. The importance of modeling in pandemic planning was highlighted, and key parameters that affect policy decision‐making were identified. Core assumptions and important activities in Canadian pandemic plans at the provincial and national levels were also discussed. Conclusions  There will be little time for thoughtful and rapid reflection once an influenza pandemic strikes, and therefore preparedness is an unavoidable priority. Modeling and simulations are key resources in pandemic planning to map out interdependencies and support complex decision‐making. Models are most effective in formulating strategies for managing public health crises when there are synergies between modelers, planners, and policymakers.