A comparison between two different automated total 25-hydroxyvitamin D immunoassay methods using liquid chromatography-tandem mass spectrometry

Introduction

Total 25-hydroxyvitamin D [25(OH)D] is the most reliable indicator of vitamin D status. In this study, we compared two automated immunoassay methods, the Abbott Architect 25-OH Vitamin D assay and the Roche Cobas Vitamin D total assay, with the liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Materials and methods

One hundred venous blood samples were randomly selected from routine vitamin D tests. Two of the serum aliquots were analyzed at the Abbott Architect i2000 and the Roche Cobas 6000’s module e601 in our laboratory within the same day. The other serum aliquots were analyzed at the LC-MS/MS in different laboratory. Passing-Bablok regression analysis and Bland-Altman plot were used to compare methods. Inter-rater agreement was analyzed using kappa (κ) analysis.

Results

The Roche assay showed acceptable agreement with the LC-MS/MS based on Passing-Bablok analysis (intercept: -5.23 nmol/L, 95% CI: -8.73 to 0.19; slope: 0.97, 95% CI: 0.77 to 1.15). The Abbott assay showed proportional (slope: 0.77, 95% CI: 0.67 to 0.85) and constant differences (intercept: 17.08 nmol/L; 95% CI: 12.98 to 21.39). A mean bias of 15.1% was observed for the Abbott and a mean bias of -14.1% was observed for the Roche based on the Bland-Altman plots. We found strong to nearly perfect agreement in vitamin D status between the immunoassays and LC-MS/MS. (κ: 0.83 for Abbott, κ: 0.93 for Roche) using kappa analysis.

Conclusion

Both immunoassays demonstrated acceptable performance, but the Roche Cobas assay demonstrated better performance than the Abbott Architect in the studied samples.Key words: 25-Hydroxyvitamin D, chromatography, immunoassay, methods, tandem mass spectrometry     

Intent-based diversification of web search results: metrics and algorithms

We study the problem of web search result diversification in the case where intent based relevance scores are available. A diversified search result will hopefully satisfy the information need of user-L.s who may have different intents. In this context, we first analyze the properties of an intent-based metric, ERR-IA, to measure relevance and diversity altogether. We argue that this is a better metric than some previously proposed intent aware metrics and show that it has a better correlation with abandonment rate. We then propose an algorithm to rerank web search results based on optimizing an objective function corresponding to this metric and evaluate it on shopping related queries.     

Investigating and counteracting popularity bias in group recommendations

Popularity bias is an undesirable phenomenon associated with recommendation algorithms where popular items tend to be suggested over long-tail ones, even if the latter would be of reasonable interest for individuals. Such intrinsic tendencies of the recommenders may lead to producing ranked lists, in which items are not equally covered along the popularity tail. Although some recent studies aim to detect such biases of traditional algorithms and treat their effects on recommendations, the concept of popularity bias remains elusive for group recommender systems. Therefore, in this study, we focus on investigating popularity bias from the view of group recommender systems, which aggregate individual preferences to achieve recommendations for groups of users. We analyze various state-of-the-art aggregation techniques utilized in group recommender systems regarding their bias towards popular items. To counteract possible popularity issues in group recommendations, we adapt a traditional re-ranking approach that weighs items inversely proportional to their popularity within a group. Also, we propose a novel popularity bias mitigation procedure that re-ranks items by incorporating their popularity level and estimated group ratings in two distinct strategies. The first one aims to penalize popular items during the aggregation process highly and avoids bias better, while the second one puts more emphasis on group ratings than popularity and achieves a more balanced performance regarding conflicting goals of mitigating bias and boosting accuracy. Experiments performed on four real-world benchmark datasets demonstrate that both strategies are more efficient than the adapted approach, and empowering aggregation techniques with one of these strategies significantly decreases their bias towards popular items while maintaining reasonable ranking accuracy.     

Modeling Unproductive Behavior in Online Homework in Terms of Latent Student Traits: An Approach Based on Item Response Theory

Homework is an important component of most physics courses. One of the functions it serves is to provide meaningful formative assessment in preparation for examinations. However, correlations between homework and examination scores tend to be low, likely due to unproductive student behavior such as copying and random guessing of answers. In this study, we attempt to model these two counterproductive learner behaviors within the framework of Item Response Theory in order to provide an ability measurement that strongly correlates with examination scores. We find that introducing additional item parameters leads to worse predictions of examination grades, while introducing additional learner traits is a more promising approach.     

Smoothing the Transition to Postsecondary Education: The Impact of the Early College Model

Developed in response to concerns that too few students were enrolling and succeeding in postsecondary education, early college high schools are small schools that blur the line between high school and college. This article presents results from a longitudinal experimental study comparing outcomes for students accepted to an early college through a lottery process with outcomes for students who were not accepted through the lottery and enrolled in high school elsewhere. Results show that treatment students attained significantly more college credits while in high school, and graduated from high school, enrolled in postsecondary education, and received postsecondary credentials at higher rates. Results for subgroups are included.     

Does innovativeness matter for international competitiveness in developing countries?: The case of Turkish manufacturing industries

We examine the determinants of export performance of firms in the Turkish manufacturing industry. Prominent differences show up between innovator and non-innovator firms in terms of the impacts of such variables as firm size, advertisement intensities, ownership structures, and composition of employees. Importance of innovations and R&D activities, conduciveness of capital intensity, and insignificance of the real wage are meaningful as far as a rational international competition policy is concerned. Results are suggestive of a technology-oriented and capital-formative development path, if Turkey is to come up with the international competitive standards.     

Evaluating the potential effect of the increased importance of the impact component in the Research Excellence Framework of the UK

The UK's Research Excellence Framework (REF) is a system that is intended to evaluate the quality of the research produced by higher education institutes (HEIs) in the UK in three areas: quality of research outputs; impact of this research beyond academia; and research environment. For the next REF, the funding bodies have reviewed the importance of the three assessment elements and decided to increase the weight of ‘impact’ to 25% (from 20% in REF2014) and decrease the weight of ‘outputs’ to 60% (from 65% in REF2014). This article first examines the relevance of some factors for the quality of impact submissions in REF2014 and finds that larger submissions and institutes with higher external research income received better impact scores in the REF. The article then examines the units of assessment (UoAs) and HEIs that benefitted from the inclusion of the impact agenda as part of REF2014 by examining the distribution of the quality-related research (QR) funding in the 2017–2018 period and finds that the QR funding gap among different UoAs tends to decrease but the gap among HEIs in most of the UoAs increased. With the increased importance of the impact agenda as a criterion for funding bodies, it is expected that research income will be concentrated in fewer universities in the future, with the increased importance of non-academic impact. This article also discusses some of the gaming strategies and long-term investment priorities that HEIs may engage in based on the new submission rules of the next REF.     

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons From a Simulation Study and an Application

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the “surface” method, the “frontier” method, the “binding-score” method, and the “fuzzy instrumental variables” method. This article uses a series of simulations to evaluate the relative performance of each of these four methods under a variety of different data-generating models. Focusing on a two-rating RDD (2RRDD), we compare the methods in terms of their bias, precision, and mean squared error when implemented as they most likely would be in practice—using optimal bandwidth selection. We also apply the lessons learned from the simulations to a real-world example that uses data from a study of an English learner reclassification policy. Overall, this article makes valuable contributions to the literature on MRRDDs in that it makes concrete recommendations for choosing among MRRDD estimation methods, for implementing any chosen method using local linear regression, and for providing accurate statistical inferences.