Publications by authors named "C Yanover"

Background And Aims: Observational healthcare data are an important tool for delineating patients' inflammatory bowel disease (IBD) journey in real-world settings. However, studies that characterize IBD cohorts typically rely on a single resource, apply diverse eligibility criteria, and extract variable sets of attributes, making comparison between cohorts challenging. We aim to longitudinally describe and compare IBD patient cohorts across multiple geographic regions, employing unified data and analysis framework.

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Unlabelled: We studied the association between non-osteoporotic fractures and future major osteoporotic fractures, using UK health records. Non-osteoporotic fractures were found to increase the risk of major osteoporotic fractures, although to a lesser extent than osteoporotic fractures. This highlights the importance of considering all previous fractures in assessing future fracture risk.

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Background: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability.

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Article Synopsis
  • Ranitidine, a commonly used medication for stomach issues, was recalled in 2020 due to the discovery of a cancer-causing impurity, raising concerns about its link to cancer among users.
  • This study aimed to investigate the cancer risk associated with ranitidine compared to other similar medications known as H2 receptor antagonists.
  • Conducted across multiple countries and databases with a large sample size, the research compared cancer incidence in new users of ranitidine against those using alternatives while accounting for various factors to ensure accurate results.
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Assessing the impact of cesarean delivery (CD) on long-term childhood outcomes is challenging as conducting a randomized controlled trial is rarely feasible and inferring it from observational data may be confounded. Utilizing data from electronic health records of 737,904 births, we defined and emulated a target trial to estimate the effect of CD on predefined long-term pediatric outcomes. Causal effects were estimated using pooled logistic regression and standardized survival curves, leveraging data breadth to account for potential confounders.

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