The impact of the changes to United Kingdom psychiatry training recruitment in 2021

Purpose The purpose of this paper is to review and critically evaluate UK psychiatry national recruitment process for 2021, which was re-structured following the COVID-19 pandemic. Additionally, this paper aims to review the empirical evidence of the selection methodologies in psychiatry recruitment. Design/methodology/approach The UK national psychiatry recruitment process is discussed, with a focus on the changes made to recruitment in 2021. The advantages and disadvantages of different selection methodologies are explored, with an emphasis on evaluating the validity, acceptability and reliability of different recruitment selection methodologies. The potential impact of the changes to psychiatry recruitment are explored. Findings The decision of the National Recruitment Office to remove certain selection methods for recruitment in 2021 may have limited their ability to choose the best candidate for the training place and be fair to the applicant. Overall, there is a lack of research into the validity of the selection methods used in psychiatry recruitment. A framework for outcome criteria relevant to psychiatry recruitment should be developed, which would allow research into selection methods and guide the NRO to examine the evidence base effectively. Originality/value This paper examined the recruitment methods used to choose doctors for psychiatry training in the UK, demonstrating that the empirical evidence base for psychiatry recruitment is limited. This paper can contribute to our understanding of selection methodologies used in psychiatry recruitment and highlights the value of different recruitment approaches for choosing the best psychiatrists of the future.

Novel Trends in Dental Color Match Using Different Shade Selection Methods: A Systematic Review and Meta-Analysis

Since color matching is considered a subjective procedure, accurate shade choice is often the most challenging stage of recreating the natural appearance of teeth. Furthermore, accurate determination of tooth color is imperative for the final outcome of dental restorations. The purpose of this research is to assess the accuracy of color match between diverse shade selection methods throughout a systematic review and meta-analysis. Two independent investigators (L.H. and R.B.) screened the literature in five electronic databases. Randomized controlled trials or in vitro papers studying the effect of using either digital shade selection or visual shade selection on the accuracy of color match were included. A total of 13 manuscripts comprised the meta-analysis. Color difference (ΔE) between restorations where the shade matching was performed by the conventional method was greater than those where the shade matching was performed by computerized methods (p = 0.007). According to the subgroup analysis, only the use of digital photographs for shade matching showed a reduction in the (ΔE) (p < 0.0001), while the use of a spectrophotometer has no advantages over the use of visual shade guide tabs (p = 0.57). On the other hand, global analysis showed that incorrect shade matching was higher when the conventional method using shade guide tabs was used (p < 0.001), irrespective of whether a spectrophotometer or a digital camera was used (p < 0.001). This study concluded that the use of digital photography and spectrophotometric measurements led to fewer color differences and less incorrect shade matching than conventional methods using color shade tabs.

Leveraging pleiotropic association using sparse group variable selection in genomics data

Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.

Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

Feature distillation and accumulated selection for automated fraudulent publisher classification from user click data of online advertising

PurposeThe problem of choosing the utmost useful features from hundreds of features from time-series user click data arises in online advertising toward fraudulent publisher's classification. Selecting feature subsets is a key issue in such classification tasks. Practically, the use of filter approaches is common; however, they neglect the correlations amid features. Conversely, wrapper approaches could not be applied due to their complexities. Moreover, in particular, existing feature selection methods could not handle such data, which is one of the major causes of instability of feature selection.Design/methodology/approachTo overcome such issues, a majority voting-based hybrid feature selection method, namely feature distillation and accumulated selection (FDAS), is proposed to investigate the optimal subset of relevant features for analyzing the publisher's fraudulent conduct. FDAS works in two phases: (1) feature distillation, where significant features from standard filter and wrapper feature selection methods are obtained using majority voting; (2) accumulated selection, where we enumerated an accumulated evaluation of relevant feature subset to search for an optimal feature subset using effective machine learning (ML) models.FindingsEmpirical results prove enhanced classification performance with proposed features in average precision, recall, f1-score and AUC in publisher identification and classification.Originality/valueThe FDAS is evaluated on FDMA2012 user-click data and nine other benchmark datasets to gauge its generalizing characteristics, first, considering original features, second, with relevant feature subsets selected by feature selection (FS) methods, third, with optimal feature subset obtained by the proposed approach. ANOVA significance test is conducted to demonstrate significant differences between independent features.

Servants of two (or more) masters: Accounting for the complexity of intraparty candidate selection methods

Intraparty candidate selection methods are the drivers of many topics of interest to political scientists. Their operationalization, however, is made complicated because they tend to involve multiple selectorates that differ in their levels of inclusiveness and centralization and that play various roles within the process. This complexity poses a challenge for large- n comparative studies. Drawing on the Political Parties DataBase Round Two to analyze candidate selection methods in 184 parties from 35 democracies, we highlight the inadequacy of the currently available measures to correctly account for this complexity in large- n studies and offer improvements on this front. Specifically, we propose a continuous measure of inclusiveness that better captures the complexity of candidate selection methods and a new measure of complexity to facilitate future analyses into this feature. We recommend that scholars in other cross-national projects consider adopting similar or improved coding strategies in order to better capture these complexities.

Improving Urban Land Cover Mapping with the Fusion of Optical and SAR Data Based on Feature Selection Strategy

Taking the Futian District as the research area, this study proposed an effective urban land cover mapping framework fusing optical and SAR data. To simplify the model complexity and improve the mapping results, various feature selection methods were compared and evaluated. The results showed that feature selection can eliminate irrelevant features, increase the mean correlation between features slightly, and improve the classification accuracy and computational efficiency significantly. The recursive feature elimination-support vector machine (RFE-SVM) model obtained the best results, with an overall accuracy of 89.17% and a kappa coefficient of 0.8695, respectively. In addition, this study proved that the fusion of optical and SAR data can effectively improve mapping and reduce the confusion between different land covers. The novelty of this study is with the insight into the merits of multi-source data fusion and feature selection in the land cover mapping process over complex urban environments, and to evaluate the performance differences between different feature selection methods.