High level of correspondence across different news domain quality rating sets
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PROVIDER: S-EPMC10500312 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe Levels of Evidence Rating System is widely believed to categorize studies by quality, with Level I studies representing the highest quality evidence. We aimed to determine the reporting quality of Randomised Controlled Trials (RCTs) published in the most frequently cited general orthopaedic journals.MethodsTwo assessors identified orthopaedic journals that reported a level of evidence rating in their abstracts from January 2003 to December 2004 by searching the instructions for authors of the highest impact general orthopaedic journals. Based upon a priori eligibility criteria, two assessors hand searched all issues of the eligible journal from 2003-2004 for RCTs. The assessors extracted the demographic information and the evidence rating from each included RCT and scored the quality of reporting using the reporting quality assessment tool, which was developed by the Cochrane Bone, Joint and Muscle Trauma Group. Scores were conducted in duplicate, and we reached a consensus for any disagreements. We examined the correlation between the level of evidence rating and the Cochrane reporting quality score.ResultsWe found that only the Journal of Bone and Joint Surgery - American Volume (JBJS-A) used a level of evidence rating from 2003 to 2004. We identified 938 publications in the JBJS-A from January 2003 to December 2004. Of these publications, 32 (3.4%) were RCTs that fit the inclusion criteria. The 32 RCTs included a total of 3543 patients, with sample sizes ranging from 17 to 514 patients. Despite being labelled as the highest level of evidence (Level 1 and Level II evidence), these studies had low Cochrane reporting quality scores among individual methodological safeguards. The Cochrane reporting quality scores did not differ significantly between Level I and Level II studies. Correlations varied from 0.0 to 0.2 across the 12 items of the Cochrane reporting quality assessment tool (p > 0.05). Among items closely corresponding to the Levels of Evidence Rating System criteria assessors achieved substantial agreement (ICC = 0.80, 95% CI:0.60 to 0.90).ConclusionOur findings suggest that readers should not assume that 1) studies labelled as Level I have high reporting quality and 2) Level I studies have better reporting quality than Level II studies. One should address methodological safeguards individually.
| S-EPMC1590046 | biostudies-literature
Project description:Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their "thematic" features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be "abnormally large," can be partially explained by the flow of news. In this sense, our results prove that there is no "excess trading," when restricting to times when news is genuinely novel and provides relevant financial information.
| S-EPMC3675119 | biostudies-literature
Project description:IntroductionData quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community.MethodsWe describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organizations.ResultsWe highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is a freely available software that provides a useful starter set of data quality rules with the ability to add additional rules. We also present results of a structured email-based interview of all participating sites that collected qualitative comments about the value of Achilles Heel for data quality evaluation.DiscussionOur analysis represents the first comparison of outputs from a data quality tool that implements a fixed (but extensible) set of data quality rules. Thanks to a common data model, we were able to compare quickly multiple datasets originating from several countries in America, Europe and Asia.
| S-EPMC5226382 | biostudies-literature
Project description:Large corpora of kinase small molecule inhibitor data are accessible to public sector research from thousands of journal article and patent publications. These data have been generated employing a wide variety of assay methodologies and experimental procedures by numerous laboratories. Here we ask the question how applicable these heterogeneous data sets are to predict kinase activities and which characteristics of the data sets contribute to their utility. We accessed almost 500,000 molecules from the Kinase Knowledge Base (KKB) and after rigorous aggregation and standardization generated over 180 distinct data sets covering all major groups of the human kinome. To assess the value of the data sets, we generated hundreds of classification and regression models. Their rigorous cross-validation and characterization demonstrated highly predictive classification and quantitative models for the majority of kinase targets if a minimum required number of active compounds or structure-activity data points were available. We then applied the best classifiers to compounds most recently profiled in the NIH Library of Integrated Network-based Cellular Signatures (LINCS) program and found good agreement of profiling results with predicted activities. Our results indicate that, although heterogeneous in nature, the publically accessible data sets are exceedingly valuable and well suited to develop highly accurate predictors for practical Kinome-wide virtual screening applications and to complement experimental kinase profiling.
| S-EPMC3569091 | biostudies-literature
Project description:We developed a dual-reputational rating shopping model to introduce public and institutional reputations. Investor's and regulator's penalty rates are described as public and institutional reputations, respectively. We achieved the available conditions of single-rating and dual-rating regulations to prevent rating inflation in this model. To examine the regulatory effects of different types of regulations on Chinese corporate bond ratings, we utilize panel ordered logit models. Theoretical analysis and empirical tests show that, when the reputation effect is low, the single-rating regulation is better at improving rating quality, and when the reputation effect is high, the dual-rating regulation induces rating agencies to provide more accurate ratings. Compared to the regulatory effects of the single-rating and the multi-rating regulations, the dual-rating regulation most effectively improves the rating quality of corporate bonds and prevents rating inflation.
| S-EPMC8638972 | biostudies-literature
Project description:Protein mass spectrometry imaging (MSI) with electrospray-based ambient ionization techniques, such as nanospray desorption electrospray ionization (nano-DESI), generates data sets in which each pixel corresponds to a mass spectrum populated by peaks corresponding to multiply charged protein ions. Importantly, the signal associated with each protein is split among multiple charge states. These peaks can be transformed into the mass domain by spectral deconvolution. When proteins are imaged under native/non-denaturing conditions to retain non-covalent interactions, deconvolution is particularly valuable in helping interpret the data. To improve the acquisition speed, signal-to-noise ratio, and sensitivity, native MSI is usually performed using mass resolving powers that do not provide isotopic resolution, and conventional algorithms for deconvolution of lower-resolution data are not suitable for these large data sets. UniDec was originally developed to enable rapid deconvolution of complex protein mass spectra. Here, we developed an updated feature set harnessing the high-throughput module, MetaUniDec, to deconvolve each pixel of native MSI data sets and transform m/z-domain image files to the mass domain. New tools enable the reading, processing, and output of open format .imzML files for downstream analysis. Transformation of data into the mass domain also provides greater accessibility, with mass information readily interpretable by users of established protein biology tools such as sodium dodecyl sulfate polyacrylamide gel electrophoresis.
| S-EPMC10515104 | biostudies-literature
Project description:Quality control and read preprocessing are critical steps in the analysis of data sets generated from high-throughput genomic screens. In the most extreme cases, improper preprocessing can negatively affect downstream analyses and may lead to incorrect biological conclusions. Here, we present PathoQC, a streamlined toolkit that seamlessly combines the benefits of several popular quality control software approaches for preprocessing next-generation sequencing data. PathoQC provides a variety of quality control options appropriate for most high-throughput sequencing applications. PathoQC is primarily developed as a module in the PathoScope software suite for metagenomic analysis. However, PathoQC is also available as an open-source Python module that can run as a stand-alone application or can be easily integrated into any bioinformatics workflow. PathoQC achieves high performance by supporting parallel computation and is an effective tool that removes technical sequencing artifacts and facilitates robust downstream analysis. The PathoQC software package is available at http://sourceforge.net/projects/PathoScope/.
| S-EPMC4429651 | biostudies-literature
Project description:Even with rapid and widespread expansion of states' quality rating and improvement systems (QRIS)-tiered frameworks that assess, communicate, and improve early childhood education (ECE) quality-there exists no population-level information regarding which providers choose to participate in these primarily voluntary systems. We use a nationally representative survey of ECE centers to examine how the characteristics of ECE centers and the communities in which they are located predict participation in QRIS to understand the scope of QRIS policy implementation and the extent to which QRIS may be equity enhancing. We find that approximately one-third of centers nationwide participated in QRIS in 2012. Selection model results reveal that participation is more likely among centers that blend multiple funding sources and who are NAEYC accredited, and in communities with high poverty rates. However, QRIS participation is less likely in communities with relatively higher proportions of Black residents. Findings raise questions about how QRISs can equitably engage programs in all communities.
| S-EPMC7543882 | biostudies-literature
Project description:As the primary arena for viral misinformation shifts toward transnational threats, the search continues for scalable countermeasures compatible with principles of transparency and free expression. We conducted a randomized field experiment evaluating the impact of source credibility labels embedded in users' social feeds and search results pages. By combining representative surveys (n = 3337) and digital trace data (n = 968) from a subset of respondents, we provide a rare ecologically valid test of such an intervention on both attitudes and behavior. On average across the sample, we are unable to detect changes in real-world consumption of news from low-quality sources after 3 weeks. We can also rule out small effects on perceived accuracy of popular misinformation spread about the Black Lives Matter movement and coronavirus disease 2019. However, we present suggestive evidence of a substantively meaningful increase in news diet quality among the heaviest consumers of misinformation. We discuss the implications of our findings for scholars and practitioners.
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Project description: Not available
| S-EPMC5134744 | biostudies-literature