BackgroundIn recent years, there has been an increase in the utilization of complementary and integrative health (CIH) care, and an increase in information-seeking behavior focused on CIH. Thus, understanding the quality of CIH information that is available on the internet is imperative. Although there have been a limited number of studies evaluating the quality of websites providing information about specific CIH-related topics, a broad evaluation of CIH websites has not been conducted.ObjectiveThis study was designed to fill that gap. We set out to assess website quality in 5 CIH domains: (1) acupuncture, (2) homeopathy, (3) massage, (4) reiki, and (5) yoga. This study aimed to 1) characterize the websites by type and quality; 2) evaluate website characteristics which may affect readers’ perceptions, specifically message content, structural features, and presentation style, and 3) investigate the extent to which harms, benefits and purposes of use are stated on websites.MethodsThis study employed a systematic search strategy to identify websites in each of the target domains to be evaluated. The websites were then classified by type, and a set of checklists focusing on quality, message content, structural features, and presentation style was used to evaluate the websites. Lastly, we performed content analysis to identify harms, benefits, and perceived purposes of use.ResultsThere were similarities across domains regarding their overall quality and their message content. Across all domains, a high proportion of websites received strong scores in terms of ownership, currency, interactivity and navigability. Scores were more variable concerning authorship, balanced presentation of information and the use of sources of information. However, there were differences regarding their structural features and presentation style. Acupuncture and reiki sites tended to include more external links, and yoga, fewer. There was variation across domains in the extent to which the websites contained domain-specific terminology. Websites tended to provide an extensive list of potential benefits, while reporting of harms was scarce.ConclusionsThis is the first study to perform a multidimensional assessment of websites in multiple CIH domains. This review showed that while there are similarities among websites of different CIH domains, there are also differences. The diverse distribution of website types suggests that, regardless of CIH domain, the public encounters information through many different types of media, and it would be useful to consider how the presentation of this content may differ depending on the medium. The characteristics for which variability exist are areas that warrant greater attention from researchers, policy makers, clinicians and patients. There is also a need to better understand how individuals may interact with CIH websites, and to develop tools to assist people to interpret the CIH-related information that they encounter.
Clinical genetic sequencing tests often identify variants of uncertain significance. One source of data that can help classify the pathogenicity of variants is familial cosegregation analysis. Identifying and genotyping relatives for cosegregation analysis can be time consuming and costly. We propose an algorithm that describes a single measure of expected variant information gain from genotyping a single additional relative in a family. Then we explore the performance of this algorithm by comparing actual recruitment strategies used in 35 families who had pursued cosegregation analysis with synthetic pedigrees of possible testing outcomes if the families had pursued an optimized testing strategy instead. For each actual and synthetic pedigree, we calculated the likelihood ratio of pathogenicity as each successive test was added to the pedigree. We analyzed the differences in cosegregation likelihood ratio over time resulting from actual versus optimized testing approaches. Employing the testing strategy indicated by the algorithm would have led to maximal information more rapidly in 30 of the 35 pedigrees (86%). Many clinical and research laboratories are involved in targeted cosegregation analysis. The algorithm we present can facilitate a data driven approach to optimal relative recruitment and genotyping for cosegregation analysis and more efficient variant classification.
Acute myeloid leukemia (AML) is a cancer of hematopoietic systems that poses high population burden, especially among pediatric populations. AML presents with high molecular heterogeneity, complicating patient risk stratification and treatment planning. While molecular and cytogenetic subtypes of AML are well described, significance of subtype-specific gene expression patterns is poorly understood and effective modeling of these patterns with individual algorithms is challenging. Using a novel consensus machine learning approach, we analyzed public RNA-seq and clinical data from pediatric AML patients (N = 137 patients) enrolled in the TARGET project.We used a binary risk classifier (Low vs. Not-Low Risk) to study risk-specific expression patterns in pediatric AML. We applied the following workflow to identify important gene targets from RNA-seq data: (1) Reduce data dimensionality by identification of differentially expressed genes for AML risk (N = 1984 loci); (2) Optimize algorithm hyperparameters for each of 4 algorithm types (lasso, XGBoost, random forest, and SVM); (3) Study ablation test results for penalized methods (lasso and XGBoost); (4) Bootstrap Boruta permutations with a novel consensus importance metric.We observed recurrently selected features across hyperparameter optimizations, ablation tests, and Boruta permutation bootstrap iterations, including HOXA9 and putative cofactors including MEIS1. Consensus feature selection from Boruta bootstraps identified a larger gene set than single penalized algorithm runs (lasso or XGBoost), while also including correlated and predictive genes from ablation tests.We present a consensus machine learning approach to identify gene targets of likely importance for pediatric AML risk. The approach identified a moderately sized set of recurrent important genes from across 4 algorithm types, including genes identified across ablation tests with penalized algorithms (HOXA9 and MEIS1). Our approach 1/15 mitigates exclusion biases of penalized algorithms (lasso and XGBoost) while obviating arbitrary importance cutoffs for other types (SVM and random forest). This approach is readily generalizable for research of other heterogeneous diseases, single-assay experiments, and high-dimensional data. Resources and code to recreate our findings are available online.Introduction 1 Acute leukemia is the most prevalent childhood cancer, accounting for 30% of childhood 2 cancers overall [1,3]. Major subtypes of pediatric acute leukemia include acute myeloid 3 leukemia (AML) and acute lymphoblastic leukemia (ALL), accounting for 15% and 85% 4 of these leukemia cases, respectively [1]. Despite improving survival rates, pediatric 5 AML remains deadlier than ALL [1]. AML is a heterogeneous cancer of the blood and 6 bone marrow myeloid stem cells that presents with numerous molecular subtypes 7 actionable for stratification and treatment. These subtypes are often based on 8 cytogenetics, molecular data, and other characteristics [2,4]. By contrast to adult AML, 9 pediatric AML is charac...
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