Plastic Surgery Program Leadership Perspectives on Doximity Residency Navigator Rankings: Do We Need a Better Guide for Prospective Applicants?

INTRODUCTION

Doximity has become integrated into the residency application process without any clear merit, comparing programs based on reputation and research. Our study aims to gather program directors' and Chiefs/Chairs' perspectives on the Doximity ranking system and to assess what a better system might entail.

METHODS

A 16-question survey was sent to 177 program directors and Chief/Chairs of plastic surgery residency programs. The questions covered three categories: (1) demographic information; (2) Doximity ranking perceptions; (3) input on characteristics of a better tool. The responses were statistically analyzed.

RESULTS

Ninety-three questionnaires were received (53%). Twenty-nine (31%) respondents represented programs in the Northeast, 23 (25%) South, 20 (21%) Midwest, and 21 (23%) West. Seventy-three (79%) respondents were male and 16 (17%) female. 90% of respondents (n = 84) believe Doximity rankings are not accurate, all indicating their institution should be ranked higher. No significant association between program geography and ranking satisfaction was observed (p = 0.75). Only 33% (n = 31) of respondents were aware of Doximity methodology. Most respondents (95%; n = 88) do not recommend the use of Doximity to medical students. Most participants (87%; n = 81) are willing to share resident case logs to inform a future tool. "Strength of technical training/preparedness" was ranked most highly as important training program qualities.

CONCLUSIONS

The results of this program leadership survey show dissatisfaction with and a lack of understanding of the Doximity system. When considering future steps, program leadership support a strength-based categorization system and sharing case logs to guide student decision-making.

Save lives or save livelihoods? A cross-country analysis of COVID-19 pandemic and economic growth

This paper studies whether containing COVID-19 pandemic by stringent strategies deteriorates or saves economic growth. Since there are country-specific factors that could affect both economic growth and deaths due to COVID-19, we first start with a cross-country analysis on identifying risk and protective factors on the COVID-19 deaths using large across-country variation. Using data on 100 countries from 3 January to 27 November 2020 and taking into account the possibility of underreporting, we find that for deaths per million population, GDP per capita, population density, and income inequality are the three most important risk factors; government effectiveness, temperature, and hospital beds are the three most important protective factors. Second, inspired by the stochastic frontier literature, we construct a measure of pandemic containment effectiveness (PCE) after controlling for country-specific factors and rank countries by their PCE scores for deaths. Finally, by linking the PCE score with GDP growth data in Quarters 2 and 3 of 2020, we find that PCE is positively associated with economic growth in major economies. Countries with average PCE scores, such as Malaysia, would gain more GDP growth by 3.47 percentage points if they could improve their PCE scores for deaths to South Korea's level in Q2 of 2020. Therefore, there is not a trade-off between lives and livelihood facing by governments. Instead, to save economy, it is important to contain the pandemic first. Our conclusion is also mainly valid for infections due to COVID-19.

How Meaningful is the Elite Quality Index Ranking?

The Elite Quality Index (EQx) attempts to measure the propensity of elites-on aggregate-to create value, rather than to rent seek. The index has attracted worldwide media and press attention. In their articles, journalists have based their analyses primarily on their own countries' position in the EQx ranking. But how meaningful is the EQx ranking? How do the uncertainties underlying some of the assumptions made in the index propagate to the country rankings? We conduct a global uncertainty and sensitivity analysis (UA and SA) of the EQx and compute Sobol' first and total order sensitivity indices using state of the art estimators, in order to scrutinise the implications of index assumptions and assess the reliability of the EQx ranking. The UA suggests that the EQx ranking of 2021 (EQx2021) is largely stable for the top 50 countries, but exhibits considerable uncertainties especially for middle and lower performing countries. The SA highlights the handling of missing data, the normalisation process and the weighting scheme as most important methodological choices, while the largest potential for improvement is observed in how raw missing indicator data is handled.

All types of Research and Publication (including in Diabetes) have shown a healthy rising trend from India in the last decade

India has moved up its publications ranking globally to fourth, and to second amongst Asiatic countries in 2020, in all subject areas. In Diabetes journals the Diabetes & Metabolic Syndrome: Clinical Research and Reviews is the fastest growing journal with the highest Cite Scoree, currently.

Assessment of the Ecotoxicity of Pollution by Potentially Toxic Elements by Biological Indicators of Haplic Chernozem of Southern Russia (Rostov region)

The content of various chemical elements such as metals, metalloids, and nonmetals in the environment is associated with natural and anthropogenic sources. It is necessary to normalize the content of metals, metalloids, and nonmetals as potentially toxic elements (PTE) in the Haplic Chernozem. The soils of the Southern Russia are of high quality and fertility. However, this type of soil, like Haplic Chernozem, is subject to contamination with a wide range of PTE. The aim of the work was to rank metals, metalloids, and nonmetals by ecotoxicity in Haplic Chernozem. To assess the ecotoxicity of chernozem, data for 15 years (2005-2020) were used. Biological indicators used to assess the ecotoxicity of Haplic Chernozem: catalase activity, cellulolytic activity, number of bacteria, Azotobacter spp. abundance, to change of length of radish's roots. Based on these biological indicators, an integral indicator of the state of Haplic Chernozem was calculated. The ecotoxicity of 23 metals (Cd, Hg, Pb, Cr, Cu, Zn, Ni, Co, Mo, Mn, Ba, Sr, Sn, V, W, Ag, Bi, Ga, Nb, Sc, Tl, Y, Yb), 5 metalloids (B, As, Ge, Sb, Te) and 2 nonmetals (F, Se) as priority pollutants. It is proposed to distinguish three hazard classes of metals, metalloids, and nonmetals to Haplic Chernozem: I class - Te, Ag, Se, Cr, Bi, Ge, Sn, Tl, Hg, Yb, W, Cd; II class - As, Co, Sc, Sb, Cu, Ni, B, Nb, Pb, Ga; III class - Sr, Y, Mo, Zn, V, Ba, Mn, F. It is advisable to use the results of the study for predictive assessment of the impact of metals, metalloids, and nonmetals on the ecological state of the soil during pollution.

Artificial Evolutionary Optimization Process to Improve the Functionality of Cell Penetrating Peptides

Crossing cellular membranes is a versatile molecular property that allows for a wide variety of peptides with cell penetrating capabilities. This broadness complicates identification of candidates suited best for a specific application. To facilitate the screening of this enormous molecular space in a supervised manner we here present a method to "breed" the desired molecules by applying the rules of Darwinian evolution. With this mate-and-check protocol, which combines an in silico evolution step with an in vitro performance test, cell penetrating peptides that are optimized for a specific task can be achieved in a few rounds of breeding. The procedure is simple and straightforward on the synthetic site but requires robust, highly reproducible and close-to-reality biological assays to yield realistic functional output. With this technology even top-performing peptides can be further improved and functionally adjusted.

Love at First Click: Surgery Residency Websites in the Virtual Era

INTRODUCTION

Frequently, a residency program's website is the first interaction students interested in surgery have with the program. In the setting of virtual interviews for residency in 2020, the online availability of program information is of heightened importance. We sought to assess how academic versus community-based general surgery residency programs compared with respect to certain details on their websites.

METHODS

A total of n = 268 surgery residency programs were investigated. Our database of website characteristics included: direct link to residency website from the American Council on Graduate Medical Education (ACGME) program page, resident research requirement, listing of residents' publications, availability of residents' demographic information, program alumni information, board pass rates, attrition rate, detail of educational/academic activities, residents' evaluation methods, diversity, and mentorship. Inter-group analyses between academic and community-based programs were performed using Pearson's Chi-squared test.

RESULTS

Academic and community-based general surgery residency program websites were compared based on twelve (12) different parameters. Statistically significant differences were observed for eight of these comparisons: direct website access from ACGME (p = 0.007), research highlighted (p < 0.001), resident research requirement (p = 0.002), resident demographic information available (p=0.004), alumni information (p = 0.005), resident evaluation methods (p = 0.016), diversity (p < 0.001), and mentorship (p = 0.012). Across these domains, academic programs had more information available on their websites than the community programs did. The program websites did not differ significantly based on the frequency of mentioning resident publications, board pass rate, attrition rate, or resident education.

CONCLUSION

Many general surgery programs are lacking detailed information on their websites. The amount of website information available on general surgery residency programs differs when comparing academic and community-based programs.

Efficiency analysis in the management of COVID-19 pandemic in India based on data envelopment analysis

Purpose: This article measured the performance of 32 states and union territories (UTs) of India against COVID-19 disease using efficiency score which was calculated by data envelopment analysis (DEA) and compared the efficiency score with the different models which are used in many articles to evaluate the efficiency of healthcare system. Here the input parameters are taken as public health expenditure in a million, number of hospitals, number of hospital beds, percentage of health workers, population density, and number of infected, and output parameters divided into good and bad categories such as the number of recovered are taken as good output. The number of death is taken as bad outputs. The modified undesirable output model is used to calculate efficiency score and compared the efficiency score with Charnes, Cooper, and Rhodes (CCR) and Banker, Charnes, and Cooper (BCC) models. Finally, the states & UTs are completely ranked with the help of efficiency score and Maximal Balance Index, and evaluated benchmarking for each states & UTs. Data Source: Secondary data were collected from Census 2011 and the Ministry of health & family welfare, Government of India on 32 stats & UTs (NHAC, 2018; NHP, 2019; COVID19India, 2021). Results: According to Undesirable model results, 16 (50%) of 32 Indian states & UTs s were found to be efficient. Among the efficient DMUs, Chandigarh is the most efficient unit and Meghalaya is the most inefficient unit. Rajasthan was the most referenced state for inefficient states. Limitation: The efficiency score is affected by changing the number of inputs and outputs. The lack of more effective parameters are used to evaluate performance and enable qualitative variable comparison.

Ranking NIH Funding of Surgical Departments Based Upon a Modified Index

BACKGROUND

The Blue Ridge Institute for Medical Research (BRIMR) reports a ranking of surgical department NIH funding each fiscal year based on more than 41,000 individual investigators. This report is used to measure the research productivity of the faculty or department. However, this method includes institutional grants awarded to Cancer Centers or Centers for Research, which do not reflect individual or departmental research. To measure the research productivity of a surgical department more directly, we created a modified BRIMR index excluding grants to cancer or research centers. We evaluated how our modified index of surgical departments compared to the rankings by BRIMR.

METHODS

Publicly available BRIMR data was filtered for all grants awarded to principal investigators in a surgical department within a medical school. All funding for Cancer Centers or Centers for Research was excluded. The remaining grants were totaled, producing a new ranking of surgical departments.

RESULTS

After excluding $42,761,752 in grants to Cancer Centers and Centers for Research, there was individual movement of 33 surgical departments on the ranking list. However, only four departments moved either up or down one quartile. No surgical department moved 2 or more quartiles.

CONCLUSIONS

NIH funding for Cancer Centers and Centers for Research comprised 10% of all NIH funding for medical school-associated surgical departments. Exclusion of this funding resulted in no significant change within surgical department quartile rankings. This suggests the BRIMR measure of research productivity does not need modification.

Soft tissue dimensional changes after alveolar ridge preservation using different sealing materials: a systematic review and network meta-analysis

Background

Alveolar ridge preservation (ARP) is a proactive treatment option aiming at attenuating post-extraction hard and soft tissue dimensional changes. A high number of different types of biomaterials have been utilized during ARP to seal the socket, but their effectiveness in terms of soft tissue outcomes has rarely been investigated and compared in the literature.

Objective

To evaluate the efficacy of different types of membranes and graft materials in terms of soft tissue outcomes (keratinized tissue width changes, vertical buccal height, and horizontal changes) after ARP, and to assign relative rankings based on their performance.

Materials and methods

The manuscript represents the proceedings of a consensus conference of the Italian Society of Osseointegration (IAO).

PUBMED (Medline), SCOPUS, Embase, and Cochrane Oral Health’s Information Specialist were utilized to conduct the search up to 06 April 2021. English language restrictions were placed and no limitations were set on publication date. Randomized controlled trials that report ARP procedures using different sealing materials, assessing soft tissue as a primary or secondary outcome, with at least 6-week follow‐up were included.

Network meta-analysis (NMA) was performed using mean, standard deviation, sample size, bias, and follow-up duration for all included studies. Network geometry, contribution plots, inconsistency plots, predictive and confidence interval plots, SUCRA (surface under the cumulative ranking curve) rankings, and multidimensional (MDS) ranking plots were constructed.

Results

A total of 11 studies were included for NMA. Overall, the level of bias for included studies was moderate. Crosslinked collagen membranes (SUCRA rank 81.8%) performed best in vertical buccal height (VBH), autogenous soft tissue grafts (SUCRA rank 89.1%) in horizontal width change (HWch), and control (SUCRA rank 85.8%) in keratinized mucosa thickness (KMT).

Conclusions

NMA confirmed that the use of crosslinked collagen membranes and autogenous soft tissue grafts represented the best choices for sealing sockets during ARP in terms of minimizing post-extraction soft tissue dimensional shrinkage.

Clinical relevance

Grafting materials demonstrated statistically significantly better performances in terms of soft tissue thickness and vertical buccal height changes, when covered with crosslinked collagen membranes. Instead, soft tissue grafts performed better in horizontal width changes. Non-crosslinked membranes and other materials or combinations presented slightly inferior outcomes.

Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology

Industrial biotechnology represents one of the most innovating and labour-productive industries with an estimated stable economic growth, thus giving space for improvement of the existing and setting up new value chains. In addition, biotechnology has clear environmental advantages over the chemical industry. Still, biotechnology’s environmental contribution is sometimes valued with controversy and societal aspects are frequently ignored. Environmental, economic and societal sustainability of various bioprocesses becomes increasingly important due to the growing understanding about complex and interlinked consequences of different human activities. Neglecting the sustainability issues in the development process of novel solutions may lead to sub-optimal biotechnological production, causing adverse environmental and societal problems proportional to the production volumes.

In the paper, sustainable metabolic engineering (SME) concept is proposed to assess and optimize the sustainability of biotechnological production that can be derived from the features of metabolism of the exploited organism. The SME concept is optimization of metabolism where economic, environmental and societal sustainability parameters of all incoming and outgoing fluxes and produced biomass of the applied organisms are considered. The extension of characterising features of strains designed by metabolic engineering methods with sustainability estimation enables ab initio improvement of the biotechnological production design.

Network Meta-Analysis Techniques for Synthesizing Prevention Science Evidence

Network meta-analysis is a popular statistical technique for synthesizing evidence from studies comparing multiple interventions. Benefits of network meta-analysis, over more traditional pairwise meta-analysis approaches, include evaluating efficacy/safety of interventions within a single framework, increased precision, comparing pairs of interventions that have never been directly compared in a trial, and providing a hierarchy of interventions in terms of their effectiveness. Network meta-analysis is relatively underutilized in prevention science. This paper therefore presents a primer of network meta-analysis for prevention scientists who wish to apply this method or to critically appraise evidence from publications using the method. We introduce the key concepts and assumptions of network meta-analysis, namely, transitivity and consistency, and demonstrate their applicability to the field of prevention science. We then illustrate the method using a network meta-analysis examining the comparative effectiveness of brief alcohol interventions for preventing hazardous drinking among college students. We provide data and code for all examples. Finally, we discuss considerations that are particularly relevant in network meta-analyses in the field of prevention, such as including non-randomized evidence.

An evaluation of performance measures for arterial brain vessel segmentation

BACKGROUND

Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation.

METHODS

To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient.

RESULTS

The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation.

CONCLUSIONS

Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.

Consistency of ranking was evaluated as new measure for prediction model stability: longitudinal cohort study

OBJECTIVE

Clinical risk prediction models are generally assessed on population level with a lack of measures that evaluate their stability at predicting risks of individual patients. This study evaluated the use of ranking as a measure to assess individual level stability between risk prediction models.

STUDY DESIGN AND SETTING

A large patient cohort (3.66 million patients with 0.11 million cardiovascular events) extracted from the Clinical Practice Research Datalink was used in the exemplar of cardiovascular disease risk prediction.

RESULTS

It was found that 15 models (including machine learning and statistical models) had similar population-level model performance (C statistics about 0.88). For patients with high absolute risks, the models were more consistent in ranking of risk predictions (interquartile range (IQR) of differences in rank percentiles -0.6 to 1.0), but inconsistent in absolute risk (IQR of differences in absolute risk -18.8 to 9.0). At low risk, the reverse was true with inconsistent ranking but more consistent absolute risk.

CONCLUSION

Consistency of ranking of individual risk predictions is a useful measure to assess risk prediction models providing complementary information to absolute risk stability. Model developing guidelines including "TRIPOD" and "PROBAST" should incorporate ranking to assess individual level stability between risk prediction models.

Community-based performance indicators for monitoring and evaluating livestock interventions

The research aimed at identifying livestock performance indicators used by farmers in Malipati community, Zimbabwe, and use them in developing a monitoring and evaluation framework for livestock interventions. Mixed methods research was used in the study. A questionnaire was administered to identify performance indicators of preference by farmers. Focus group discussions were done to rank performance indicators. Data analysis was done using SPSS version 25, and data were analysed using the ranking matrix. Scientific validity of performance indicators was determined through literature review. The study concluded that performance indicators of importance in poultry, cattle, goats/sheep, and donkeys were egg production, milk yield, kidding/lambing interval, and animal power, respectively. All performance indicators identified by farmers in Malipati are scientifically valid and were used in the development of the monitoring and evaluation framework.

Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization

Social media platforms like Twitter have become an easy portal for billions of people to connect and exchange their thoughts. Unfortunately, people commonly use these platforms to share misinformation which can influence other people adversely. The spread of misinformation is unavoidable in an extraordinary situation like Covid-19, and the consequences can be dreadful. This paper proposes a two-step ranking-based misinformation detection (RMiD) technique. Firstly, a novel ranking-based approach leveraging the scalable information retrieval infrastructure is applied to detect misinformation from a huge collection of unlabelled tweets based on a related but very small labelled misinformation data set. Secondly, the identified misinformation tweets are represented as a coupled matrix tensor model and Nonnegative Coupled Matrix Tensor Factorization is applied to learn their spatio-temporal topic dynamics. The experimental analysis shows that RMiD is capable of detecting misinformation with better coverage and less noise in comparison with existing techniques. Moreover, the coupled matrix tensor representation has improved the quality of topics discovered from unlabelled data up to 4% by leveraging the semantic similarity of terms in labelled data.

SUPPLEMENTARY INFORMATION

The online version supplementary material available at 10.1007/s13278-021-00767-7.

Research ethics: a profile of retractions from world class universities

This study aims to profile the scientific retractions published in journals indexed in the Web of Science database from 2010 to 2019, from researchers at the top 20 World Class Universities according to the Times Higher Education global ranking of 2020. Descriptive statistics, Pearson's correlation coefficient, and simple linear regression were used to analyze the data. Of the 330 analyzed retractions, Harvard University had the highest number of retractions and the main reason for retraction was data results. We conclude that the universities with a higher ranking tend to have a lower rate of retraction.

Methodological approaches for analysing data from therapeutic efficacy studies

Several anti-malarial drugs have been evaluated in randomized clinical trials to treat acute uncomplicated Plasmodium falciparum malaria. The outcome of anti-malarial drug efficacy studies is classified into one of four possible outcomes defined by the World Health Organization: adequate clinical and parasitological response, late parasitological failure, late clinical failure, early treatment failure. These four ordered categories are ordinal data, which are reduced to either a binary outcome (i.e., treatment success and treatment failure) to calculate the proportions of treatment failure or to time-to-event outcome for KaplanMeier survival analysis. The arbitrary transition from 4-level ordered categories to 2-level type categories results in a loss of statistical power. In the opinion of the authors, this outcome can be considered as ordinal at a fixed endpoint or at longitudinal endpoints. Alternative statistical methods can be applied to 4-level ordinal categories of therapeutic response to optimize data exploitation. Furthermore, network meta-analysis is useful not only for direct comparison of drugs which were evaluated together in a randomized design, but also for indirect comparison of different artemisinin-based combinations across different clinical studies using a common drug comparator, with the aim to determine the ranking order of drug efficacy. Previous works conducted in Cameroonian children served as data source to illustrate the feasibility of these novel statistical approaches. Data analysis based on ordinal end-point may be helpful to gain further insight into anti-malarial drug efficacy.

Factors influencing medical student choices in the integrated vascular surgery match: Implications for future post-pandemic residency matches

OBJECTIVE

Integrated vascular surgery residency is among the most competitive specialties, but little is known about the applicant perspective. The coronavirus disease 2019 outbreak impacted the 2021 integrated vascular surgery residency match because of travel restrictions. We sought to better understand pre-pandemic applicant recruitment strategies, logistics of away rotations, and the residency interview process to identify areas for improvement in the application process.

METHODS

An anonymous survey was sent to matched students in 2020, inquiring about motivations for pursuing vascular surgery (VS), logistic of away rotations and interviews, and factors influencing students' rank lists.

RESULTS

Seventy of the 73 matched students completed the survey (95.9% response rate). The median age was 27 (range, 25-41); 32.9% were female, 91.4% were U.S. medical students, and 77.1% were from institutions with a VS training program. Factors most strongly influencing the decision to choose VS as a career were interest in open vascular procedures, endovascular procedures, perceived job satisfaction, emerging technologies, and influence of a mentor. The prospect of the job market, future salary, and competitiveness of the application process had the least impact. Of the matched students, 82.9% completed an away rotation (median, 2; range, 1-4), with 51.7% of students paying a total cost of more than $2500. Fifty percent of students matched either at their home institution or where they had performed an away rotation. Students reported application submissions to a median of 50 programs (range, 1-70) and interviewed at 17 (range, 1-28), with 40% of students paying a total of more than $4000 for interview costs. The most significant factors affecting students' rank lists included program culture, open aortic surgical volume, geography, and complex endovascular procedure volume. Tours of facilities, resident salary, and male/female distribution had the least importance.

CONCLUSIONS

Successfully matched applicants in 2020 prioritized operative case volume and program collegiality when ranking programs. Despite their high cost, away rotations played an important role in the Match, suggesting that time spent at potential institutions allowed ideal assessment of factors for students. The high average number of away rotations and in-person interviews performed in 2019-2020 was limited for the 2021 Match due to coronavirus disease 2019 restrictions. Programs will have to continue developing creative alternatives or additions to away rotations and the application processes to assure continued success in future post-pandemic Match cycles.

miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.

Improved cluster ranking in protein-protein docking using a regression approach

We develop a Regression-based Ranking by Pairwise Cluster Comparisons (RRPCC) method to rank clusters of similar protein complex conformations generated by an underlying docking program. The method leverages robust regression to predict the relative quality difference between any pair or clusters and combines these pairwise assessments to form a ranked list of clusters, from higher to lower quality. We apply RRPCC to clusters produced by the automated docking server ClusPro and, depending on the training/validation strategy, we show improvement by 24-100% in ranking acceptable or better quality clusters first, and by 15-100% in ranking medium or better quality clusters first. We compare the RRPCC-ClusPro combination to a number of alternatives, and show that very different machine learning approaches to scoring docked structures yield similar success rates. Finally, we discuss the current limitations on sampling and scoring, looking ahead to further improvements. Interestingly, some features important for improved scoring are internal energy terms that occur only due to the local energy minimization applied in the refinement stage following rigid body docking.

Predicting the 2016 US News & World Report rankings using a reputation and prestige model

INTRODUCTION

The objective was to determine institutional, faculty, and student attributes predictive of the 2016 US News & World Report (USNWR) pharmacy rankings and to explore if student attributes modify program rankings.

METHODS

Institutional attributes and student and faculty resources and outcomes were obtained from various sources. Regression analyses predicted rankings.

RESULTS

USNWR rankings were higher for older programs, those located at an academic health center or classified as a research-intensive institution, and members of a Power Five athletic conference. Number of fulltime equivalent faculty, Pharmacy College Admissions Test composite percentile, pharmacy practice h-index score, funding rank, and first time North American Pharmacist Licensure Examination (NAPLEX) pass rate predicted higher rankings. Nearly 45% of programs in the empiric models left or entered the top 25 rankings compared with the existing USNWR method (range 31 place increase to 22 place decrease). Among USNWR top 25 ranked programs, 16 remained in the top 25 in all four empiric models and three were not included in any of the models. Six USNWF unranked programs moved into the top 25 by one or more of the empiric models.

CONCLUSIONS

Faculty and student attributes significantly impact program rankings, while impact of institutional attributes is negligible if independent of student and faculty attributes. Faculty numbers and productivity influence USNWR rankings more than student academic preparation and success on NAPLEX. These findings will inform efforts to improve the validity of the USNWR rankings and identify programs that are both prestigious and of high quality.

Prioritisation for future surveillance, prevention and control of 98 communicable diseases in Belgium: a 2018 multi-criteria decision analysis study

Background

National public health agencies are required to prioritise infectious diseases for prevention and control. We applied the prioritisation method recommended by the European Centre for Disease Prevention and Control to rank infectious diseases, according to their relative importance for surveillance and public health, to inform future public health action in Belgium.

Methods

We applied the multi-criteria-decision-analysis approach. A working group of epidemiologists and statisticians from Belgium (n = 6) designed a balanced set of prioritisation criteria. A panel of Belgian experts (n = 80) allocated in an online survey each criteria a weight, according to perceived relative importance. Next, experts (n = 37) scored each disease against each criteria in an online survey, guided by disease-specific factsheets referring the period 2010–2016 in Belgium. The weighted sum of the criteria’s scores composed the final weighted score per disease, on which the ranking was based. Sensitivity analyses quantified the impact of eight alternative analysis scenarios on the top-20 ranked diseases. We identified criteria and diseases associated with data-gaps as those with the highest number of blank answers in the scoring survey. Principle components of the final weighted score were identified.

Results

Working groups selected 98 diseases and 18 criteria, structured in five criteria groups. The diseases ranked highest were (in order) pertussis, human immunodeficiency virus infection, hepatitis C and hepatitis B. Among the five criteria groups, overall the highest weights were assigned to ‘impact on the patient’, followed by ‘impact on public health’, while different perceptions were identified between clinicians, microbiologists and epidemiologists. Among the 18 individual criteria, ‘spreading potential’ and ‘events requiring public health action’ were assigned the highest weights. Principle components clustered with thematic disease groups. Notable data gaps were found among hospital-related diseases.

Conclusions

We ranked infectious diseases using a standardised reproducible approach. The diseases ranked highest are included in current public health programs, but additional reflection for example about needs among risk groups is recommended. Cross-reference of the obtained ranking with current programs is needed to verify whether resources and activities map priority areas. We recommend to implement this method in a recurrent evaluation cycle of national public health priorities.

Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic

Understanding attention dynamics on social media during pandemics could help governments minimize the effects. We focus on how COVID-19 has influenced the attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic. We study the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches. We show how the specific events, measures and developments during the epidemic affected the emergence of different kinds of hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced. Then very rapidly a situation was reached where COVID-related hashtags occupied 30-70% of the HSL, however, with changing content. We give an analysis of how the hashtag topics changed during the investigated time span and conclude that there are three periods separated by February 12 and March 12. In period 1, we see strong topical correlations and clustering of hashtags; in period 2, the correlations are weakened, without clustering pattern; in period 3, we see a potential of clustering while not as strong as in period 1. We further explore the dynamics of HSL by measuring the ranking dynamics and the lifetimes of hashtags on the list. This way we can obtain information about the decay of attention, which is important for decisions about the temporal placement of governmental measures to achieve permanent awareness. Furthermore, our observations indicate abnormally higher rank diversity in the top 15 ranks on HSL due to the COVID-19 related hashtags, revealing the possibility of algorithmic intervention from the platform provider.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00263-0.