Citations

Background: Cerebral cavernous malformation is a genetic neurovascular disorder causing aberrant angiogenesis and lesion development within capillary beds. Lesions exhibit impaired endothelial barrier function, causing blood-brain barrier leakage and hemorrhage. Quantitative MRI biomarkers (dynamic contrast-enhanced quantitative permeability (DCEQP) and quantitative susceptibility mapping (QSM)) have been shown to correlate with CCM disease progression as measures of barrier leakage and hemorrhagic iron deposition, and there is interest in utilizing these biomarkers as endpoints in clinical trials. However, their consistencies across multiple platforms and institutions has not yet been rigorously verified. Materials and Methods: Instruments used were a 3T Philips Ingenia at University of Chicago and a 3T Siemens Prisma at Northwestern University. QSM was assessed by five balloons filled with varying concentrations of MultiHance, corresponding to magnetic susceptibilities of 0, 0.1, 0.2, 0.4, and 0.8, surrounded by an agarose gel. Permeability was assessed by the injection of a bolus of MultiHance through a coiled hose inside a water-filled shell, and the consistency of the peak-to-peak ratio of the curves at the input and output was measured. Each biomarker was assessed by accuracy (to expected values, Pearson correlation), precision (between repeated measurements, paired t-test), and reproducibility (between instruments, paired t-test). Results: QSM measured with an accuracy of r 2 = 0.997 on the Philips Achieva and r 2 = 0.999 on the Siemens Prisma. Each scanner had adequate precision between measurements, showing no significant difference by a paired t-test (p = 0.66 Achieva, p = 0.45 Prisma), and the reproducibility between scanners also showed no significant difference (p = 0.89). The peak-to-peak ratio on DCEQP scans was accurate (r 2 = 0.998), precise between measurements (p = 0.54), and repeatable between instruments (p = 0.49). Conclusion: Quantitative imaging biomarkers DCEQP and QSM are accurate, consistent across multiple measurements, and between platforms and will be useful for future clinical trials in CCM.

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.

Objective There exists a paucity of work in the development and testing of theoretical models specific to childhood health disparities even though they have been linked to the prevalence of adult health disparities including high rates of chronic disease. We conducted a systematic review and thematic analysis of existing models of health disparities specific to children to inform development of a unified conceptual framework. Methods We systematically reviewed articles reporting theoretical or explanatory models of disparities on a range of outcomes related to child health. We searched Ovid Medline In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus (database inception to 9 July 2015). A metanarrative approach guided the analysis process. Results A total of 48 studies presenting 48 models were included. This systematic review found multiple models but no consensus on one approach. However, we did discover a fair amount of overlap, such that the 48 models reviewed converged into the unified conceptual framework. The majority of models included factors in three domains: individual characteristics and behaviours (88%), healthcare providers and systems (63%), and environment/community (56%), . Only 38% of models included factors in the health and public policies domain. Conclusions A disease-agnostic unified conceptual framework may inform integration of existing knowledge of child health disparities and guide future research. This multilevel framework can focus attention among clinical, basic and social science research on the relationships between policy, social factors, health systems and the physical environment that impact children's health outcomes.