Rank Candidate Research Articles

The impact of interviewer characteristics on residency candidate scores in Emergency Medicine: a brief report

Background: At the conclusion of residency candidate interview days, faculty interviewers commonly meet as a group to reach conclusions about candidate evaluations based on shared information. These conclusions ultimately translate into rank list position for The Residency Match. The primary objective is to determine if the post-interview discussion influences the final scores assigned by each interviewer, and to investigate whether interviewer characteristics are significantly associated with the likelihood of changing their score. Based on Foucault’s ‘theory of discourse’ and Bourdieu’s ‘social capital theory,’ we hypothesized that interviewer characteristics, and the discourse itself, would contribute to score changes after a post-interview discussion regarding emergency medicine residency candidates. Methods: We conducted a cross-sectional observational study of candidate scores for all candidates to a four-year emergency medicine residency program affiliated with Yale University School of Medicine during a single application cycle. The magnitude and direction of score changes, if any, after group discussion were plotted and grouped by interviewer academic rank. We created a logistic regression model to determine odds that candidate scores changed from pre- and post-discussion ratings related to specific interviewer factors. Results: A total of 24 interviewers and 211 candidates created 471 unique interviewer-candidate scoring interactions, with 216 (45.8%) changing post-discussion. All interviewers ranked junior to professor were significantly more likely to change their score compared to professors. Interviewers who were women had significantly lower odds of changing their individual scores following group discussion (p=0.020; OR 0.49, 95% CI 0.26-0.89). Conclusions: Interviewers with lower academic rank had higher odds of changing their post-discussion scores of residency candidates compared to professors. Future work is needed to further characterize the influencing factors and could help create more equitable decision processes during the residency candidate ranking process.

Priorities for narrowing the yield gap and increasing farming systems resilience in the Fiji sugar industry

Sugarcane (Saccharum officinarum L.) is the single most important agricultural industry in Fiji and occupies half of the nation's cropped area while supporting 25% of the labour force. However, the industry is facing comprehensive challenges across the supply chain, underlain by declining productivity and changing climate. The purposes of this study were to: (1) use model simulation to determine the potential of conservation agriculture (CA) practices to narrow yield gaps in sugarcane production systems under historical and projected climates in a case study; (2) identify and rank candidate practices that would contribute to narrowing such yield gaps; and (3) identify the limitations of the modelling approach and make recommendations for improving it. We simulated nine scenarios of CA practices with potential to reduce the estimated yield gap (55 ​Mg ​ha-1) by increasing nitrogen (N) fertiliser rates (30 ​Mg ​ha−1), liming (25 ​Mg ​ha−1), improved weed control (5–9 ​Mg ​ha−1), replacing uncropped fallow with legume crops (2–3 ​Mg ​ha−1), crop cycle duration (0 ​Mg ​ha−1), and retaining crop residues (−2 ​Mg ​ha−1). The yield benefit of CA practices was similar under projected climates that were warmer and wetter or drier, or hotter and wetter. For a projected climate that was hotter and drier, the greatest yield benefit was obtained from liming, weed control, and retaining crop residues. The study was limited by the availability of daily weather data, and it would be complemented by additional studies incorporating more local crops, evaluation of barriers to mechanisation needed to implement new practices, and economic assessment of scenarios.

Supplemental ERAS application for radiology residency match: A primer

The supplemental ERAS application that includes three components: past experiences, geographic preferences, and program signals was introduced in 2022 to complement the standard ERAS CV material. The goal was to help programs identify optimal candidates to interview and to improve the chances of applicants being invited for interviews at programs that align with their goals and interests. Based on limited data, Program signal is the most emphasized component by the programs. Applicants should realize that programs have used signals to determine who to interview (aligned with AAMC guidance), and to determine the program's candidate rank list (contrary to AAMC guidance). We have herein suggested options for leveraging benefits from the ERAS supplemental application which has now been incorporated into the full ERAS application.

Incorporating Effects of Time Accrued on the Waiting List into Lung Transplantation Survival Models.

Rationale: U.S. lung transplant mortality risk models do not account for patients' disease progression as time accrues between mandated clinical parameter updates. Objectives: To investigate the effects of accrued waitlist (WL) time on mortality in lung transplant candidates and recipients beyond those expressed by worsening clinical status and to present a new framework for conceptualizing mortality risk in end-stage lung disease. Methods: Using Scientific Registry of Transplant Recipients data (2015-2020, N = 12,616), we modeled transitions among multiple clinical states over time: WL, posttransplant, and death. Using cause-specific and ordinary Cox regression to estimate trajectories of composite 1-year mortality risk as a function of time from waitlisting to transplantation, we quantified the predictive accuracy of these estimates. We compared multistate model-derived candidate rankings against composite allocation score (CAS) rankings. Measurements and Main Results: There were 11.5% of candidates whose predicted 1-year mortality risk increased by >10% by day 30 on the WL. The multistate model ascribed lower numerical rankings (i.e., higher priority) than CAS for those who died while on the WL (multistate mean; median [interquartile range] ranking at death, 227; 154 [57-334]; CAS median [interquartile range] ranking at death, 329; 162 [11-668]). Patients with interstitial lung disease were more likely to have increasing risk trajectories as a function of time accrued on the WL compared with other lung diagnoses. Conclusions: Incorporating the effects of time accrued on the WL for lung transplant candidates and recipients in donor lung allocation systems may improve the survival of patients with end-stage lung diseases on the individual and population levels.

Multiscale influences on Elk (Cervus canadensis) calving site selection across a fragmented Appalachian landscape

Abstract Due to the vulnerability of neonatal ungulates, selection of a birth site can have important implications for offspring survival and thus fitness of the mother. We studied parturition site selection in cow Elk in southeastern Kentucky, United States, using a use–availability framework to evaluate the effects of landscape variables sampled at multiple spatial grains on the relative probability of use of parturition sites. We identified 81 Elk parturition sites during May–Aug 2020–2022 and fit several candidate resource selection function models using a sample of 24,314 random locations to characterize habitat availability. Using an information-theoretic approach to rank candidate models, we identified two top-performing models (cumulative ωi = 0.97), which indicated that at fine spatial grains (~10- and 30-m pixels) parturient cow Elk selected for intermediate canopy cover and lower terrain ruggedness compared to available locations. At coarse grains (250–1,000-m buffers/neighborhoods), Elk selected against vegetation greenness/biomass, for higher topographic positions, for closer proximity to major roads, and with higher solar radiation potential. We also found evidence that Elk responded positively to forest/open edge densities at coarse grains, demonstrating that calving habitat throughout the Kentucky Elk Restoration Zone is associated with heterogeneous landscapes resulting from surface mine reclamation. As a result, habitat management actions should aim to increase patchy, early successional shrub cover on gentle topography.

Personalized Re-ranking for Recommendation with Mask Pretraining

Re-ranking is to refine the candidate ranking list of recommended items, such that the re-ranked list attracts users to purchase or click more items than the candidate one without re-ranking. Items in the candidate list are often ranked by their relevance to users’ interests. It is thus important to exploit the mutual influence between items in the re-ranking process. Existing re-ranking models focus on only the pairwise influence between two items, and have limited capability to exploit the local mutual influence in a group of items. Users often show successive interests on a group of relevant items, e.g., mobile phone, phone covers, wireless headset, namely scene. We propose a novel re-ranking model that jointly exploits the local mutual influence in scenes and the global mutual influence between different scenes. Scene representations are learned by GNN and multi-head attention, where GNN aims to learn local mutual influence while multi-head attention is to learn global mutual influence. To study the interaction between users and scenes, matrix factorization on users is utilized to obtain the user preference, which can be further applied to scenes to compute the scene scores. The final re-ranking list is generated by sorting the predicted scores of all scenes. To further mine user history information and item related user information, we also develop the extension pretraining module which relies on mask mechanism to support users and items high-quality embedding generation. We conduct a comprehensive evaluation on several real-world datasets. The experimental results demonstrate that our model substantially outperforms existing approaches.

Improving relative performance evaluation process of employees within a peer group

PurposeRelative performance evaluation (RPE) is a widely practiced employee appraisal process in the services industry. In a global delivery model, teams are spread across different geographical locations. The team members work on various tasks under the guidance of different managers and at times under more than one manager for performing the same task. Such complexities make RPE of the team members quite challenging. The paper proposes a methodical step-by-step approach to simplify the evaluation process without compromising on the rigour.Design/methodology/approachRPE has followed three different approaches. First is the traditional way, wherein evaluators had a common meeting to discuss and arrive at relative evaluation and ranking of members of the peer group employees. In the second, the number of evaluators and employees in a peer group were split in to 2 subgroups. The evaluators provided independent ratings and rankings. Simple mathematical tool then derived the combined ranking. In the third approach, each evaluator evaluated each employee in the peer group and provided the relative ranking for each employee. Again, mathematical tools provided the final ranking considering inputs from all evaluators. All the three evaluation approaches were analysed through an inter-rater agreement method.FindingsAll the three approaches for evaluation provided similar results giving confidence that less time-consuming methods could be adopted by evaluators without compromising on the rigour of the evaluation. The outcome of the exercise proved effective as the complaints reaching the ombudsmen reduced as compared to the earlier years. Considerable evaluation time was also saved. The study described in this paper is carried out in a non-unionized, Indian private sector services firm. Its effectiveness in other set ups is yet to be tested.Research limitations/implicationsThe research is carried out in the Indian Engineering services firm operating in the Knowledge based sector. Though study results are encouraging, the adaptability of methodology across different sectors and geographies is yet to be tested. More broad based studies are needed to evaluate suitability across firms and regions.Practical implicationsRelative evaluation exercise is challenging for evaluators. Although openness in evaluation is desired, it also makes evaluators uncomfortable in appearing to be taking sides or being opposing a candidate's ranking. The proposed approach brings in anonymity to each evaluator without scarifying individual evaluation.Social implicationsThe proposed methodology can be deployed across different services industries as the proposed methodology is business domain agnostic. It can be easily ported and tailored to align with an individual organization's evaluation philosophy. The suitability and effectiveness of the method can be studied under various types of firms like manufacturing, private, public, NGO, labour oriented, etc. As the proposed method reduces efforts, the stake holders can focus on understanding the relation between employee performance measurement, employee engagement, and long-term outcomes related to employee performance evaluation.Originality/valueThe proposed employee evaluation method leverages inter-rater reliability and agreement tool as a consensus approach to the relative performance ranking exercise. Such an approach to relative performance ranking is original as no prior studies with such an approach are found in the existing Literature.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study

ObjectivesTo efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. DesignWe designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. ResultsFrom 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81–0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52–0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). ConclusionsThe machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification.

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Sustainability Performance Evaluation of Solar Panels Using Multi Criteria Decision Making Techniques

Researchers from all around the world had worked tirelessly to find ways to lower the cost of solar panels, create more efficient new goods, boost their fuel efficiency, and create innovations and largely dependent on photovoltaic system technology. When compared to other forms of non-conventional energy, such as wind and tidal, solar has been one of the most widely employed resources. To harness the power of the sun using photovoltaics, one needs a photovoltaic system. Research into improving the cost-effectiveness of solar panels, which play a crucial part in photovoltaic systems, is a global endeavour. The process of choosing solar panels is nuanced, encompassing a wide range of subjective and objective considerations. To choose the best solar cell for a PV array, we use the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) techniques. Using the Analytical Hierarchy Process (AHP) and a resemblance to ideal result ordering technique, the study’s goals were satisfied. VIKOR and other MCDM tools like the AHP and TOPSIS are used to rank candidates in terms of performance. An in-depth case study was conducted using six different kinds of solar panels to show how well the approaches work.

Optimal intensity measure selection and probabilistic seismic demand model of pile group supported bridges in sandy soil considering variable scour effects

Scour of pile group foundations is a common hazard for cross-river bridges and can produce considerable damage to piles in earthquake-prone regions. The fragility-based performance assessment for pile group supported bridges subjected to earthquake and scour hazards has drawn increasing attention from engineering practice and academic communities. Such assessment requires numerical models that are computationally efficient and accurate in describing the mechanical behavior associated with the complex soil-pile interaction of these soil-bridge systems. Additionally, selecting an optimal ground motion intensity measure (IM) is generally required for such assessments. However, modeling the pile group effect for seismically excited pile groups and the optimal IM for the scoured bridges have not been well addressed in previous related studies. To this end, a practical pile group effect modeling approach is adopted in this study and validated by a set of shake table tests on scoured pile group bridge available in the literature. Based on the validated numerical modeling approach for the soil pile interaction, a series of coupled soil-bridge models are generated considering the structural and soil parameter uncertainty and analyzed in the OpenSees platform. The optimal IM for various engineering demand parameters (EDPs) is identified from thirty-nine scalar IM candidates through the criteria of efficiency, practicality, sufficiency, and hazard computability. Finally, a straightforward form of probabilistic seismic demand model (PSDM), incorporating the effect of scour depth changes, is developed for pile group supported bridges. Results show that the increase of scour depth significantly increases the curvature demand of a pile group and statistically reduces the embedded depth of the belowground plastic hinge of the pile shaft. The rank of optimal IM candidates directly depends on the selection criteria of an optimal IM and the selection of EDP. In addition, the scour depth slightly affects the rank of optimal IMs. Generally, VSI, PGV, and Sv,1.0 are recommended for the probabilistic seismic demand analysis of pile group supported bridges in sandy soils with scour potentials.

Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min–Max Optimization and Its Application to Berthing Control Tasks

In this study, we consider a continuous min–max optimization problem minx∈ 𝕏maxy∈ 𝕐f(x, y) whose objective function is a black-box. We propose a novel approach to minimize the worst-case objective functionF(x) = maxy∈ 𝕐f(x, y) directly using a covariance matrix adaptation evolution strategy in which the rankings of solution candidates are approximated by our proposed worst-case ranking approximation mechanism. We develop two variants of worst-case ranking approximation combined with a covariance matrix adaptation evolution strategy and approximate gradient ascent as numerical solvers for the inner maximization problem. Numerical experiments show that our proposed approach outperforms several existing approaches when the objective function is a smooth strongly convex–concave function and the interaction betweenxandyis strong. We investigate the advantages of the proposed approach for problems where the objective function is not limited to smooth strongly convex–concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty.

Rank Aggregation Using Scoring Rules

To aggregate rankings into a social ranking, one can use scoring systems such as Plurality, Veto, and Borda. We distinguish three types of methods: ranking by score, ranking by repeatedly choosing a winner that we delete and rank at the top, and ranking by repeatedly choosing a loser that we delete and rank at the bottom. The latter method captures the frequently studied voting rules Single Transferable Vote (aka Instant Runoff Voting), Coombs, and Baldwin. In an experimental analysis, we show that the three types of methods produce different rankings in practice. We also provide evidence that sequentially selecting winners is most suitable to detect the "true" ranking of candidates. For different rules in our classes, we then study the (parameterized) computational complexity of deciding in which positions a given candidate can appear in the chosen ranking. As part of our analysis, we also consider the Winner Determination problem for STV, Coombs, and Baldwin and determine their complexity when there are few voters or candidates.

Repair Is Nearly Generation: Multilingual Program Repair with LLMs

Most programmers make mistakes when writing code. Some of these mistakes are small and require few edits to the original program – a class of errors recently termed last mile mistakes. These errors break the flow for experienced developers and can stump novice programmers. Existing automated repair techniques targeting this class of errors are language-specific and do not easily carry over to new languages. Transferring symbolic approaches requires substantial engineering and neural approaches require data and retraining. We introduce RING, a multilingual repair engine powered by a large language model trained on code (LLMC) such as Codex. Such a multilingual engine enables a flipped model for programming assistance, one where the programmer writes code and the AI assistance suggests fixes, compared to traditional code suggestion technology. Taking inspiration from the way programmers manually fix bugs, we show that a prompt-based strategy that conceptualizes repair as localization, transformation, and candidate ranking, can successfully repair programs in multiple languages with minimal effort. We present the first results for such a multilingual repair engine by evaluating on 6 different languages and comparing performance to language-specific repair engines. We show that RING can outperform language-specific repair engines for three of these languages.

GAN-Based Domain Inference Attack

Model-based attacks can infer training data information from deep neural network models. These attacks heavily depend on the attacker's knowledge of the application domain, e.g., using it to determine the auxiliary data for model-inversion attacks. However, attackers may not know what the model is used for in practice. We propose a generative adversarial network (GAN) based method to explore likely or similar domains of a target model -- the model domain inference (MDI) attack. For a given target (classification) model, we assume that the attacker knows nothing but the input and output formats and can use the model to derive the prediction for any input in the desired form. Our basic idea is to use the target model to affect a GAN training process for a candidate domain's dataset that is easy to obtain. We find that the target model may distort the training procedure less if the domain is more similar to the target domain. We then measure the distortion level with the distance between GAN-generated datasets, which can be used to rank candidate domains for the target model. Our experiments show that the auxiliary dataset from an MDI top-ranked domain can effectively boost the result of model-inversion attacks.

Variance component estimation and genome-wide association of predicted methane production in crossbred beef steers.

Enteric methane is a potent greenhouse gas and represents an escape of energy from the ruminant digestive system. Additive genetic variation in methane production suggests that genetic selection offers an opportunity to diminish enteric methane emissions. Logistic and monetary difficulties in directly measuring methane emissions can make genetic evaluation on an indicator trait such as predicted methane production a more appealing option, and inclusion of genotyping data can result in greater genetic progress. Three predicted methane production traits were calculated for 830 crossbred steers fed in seven groups. The methane prediction equations used included mathematical models from Ellis et al. (2007), Mills et al. (2003), and IPCC (2019). Pearson correlations between the traits were all greater than 0.99, indicating that each prediction equation behaved similarly. Further, the Spearman correlations between the estimated breeding values for each trait were also 0.99, which suggests any of the predicted methane models could be used without substantially changing the ranking of the selection candidates. The heritabilities of Ellis, Mills, and IPCC predicted methane production were 0.60, 0.62, and 0.59, respectively. A genome-wide association study identified one single nucleotide polymorphism (SNP) that reached the threshold for significance for all of the traits on chromosome 7 related to oxidoreductase activity. Additionally, the SNP slightly below the significance threshold indicate genes related to collagen, intracellular microtubules, and DNA transcription may play a role in predicted methane production or its component traits.

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases.

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

Community-aware empathetic social choice for social network group decision making

To study how interdependent and self-interested decision makers collectively make a choice in highly complex social environments, this paper presents a novel and powerful Community-Aware Empathetic Social Choice (CAESC) model, which considers decision makers in social networks as autonomy-oriented agents, who derive utility based on both their own intrinsic preferences, and empathetic preferences determined by the satisfaction of their intra-community neighbors. We devise optimization approaches for identifying community structures with local (global) social welfare maximizing under two varieties of CAESC models: The local CAESC model first identifies the non-cooperative behaviors by calculating the structure-preference coordination coefficients between pairwise decision makers, then employs the Logit response like dynamics process in a carefully defined potential game to find the optimal community structure that maximizes the local social welfare. To find the optimal community structure that maximizes the global social welfare, the global CAESC model is composed of three coupled phases during each stage: (i) opinion evolution within each community using the classic Jacobi method; (ii) structure-preference coordination matrix updating based on the current opinion vector; and (iii) local optimal community structure updating based on the local CAESC model. Extensive experiments on both randomly generated and real-world social networks with synthetic and real-world preferences confirm that neglecting the empathetic effect and community structure usually yields sub-optimal group decisions which degrade the social welfare of network members. Our experiments also show that, the higher societal empathy, the rougher preference distribution and the denser network structure will facilitate the maximal social welfare reaching in both local and global CAESC models; meanwhile, by comparing with the typical empathetic decision making models and some recent proposed consensus reaching methods, our proposed approaches show relatively significant advantages in terms of candidate ranking and decision efficiency.

Worldwide organ allocation systems for pediatric lung transplantation.

Pediatric lung transplantation (LTX) remains limited by the scarcity of small donor lungs, particularly in less populated parts of the world. Optimal organ allocation, including the prioritization and ranking of pediatric LTX candidates, and the appropriate matching of pediatric donors to recipients have been crucial elements in improving pediatric LTX outcomes. We aimed to elucidate the various pediatric lung allocation practices worldwide. A global survey of current pediatric solid organ transplantation deceased donation allocation practices was conducted by the International Pediatric Transplant Association (IPTA), and these policies were subsequently analyzed if publicly available, with focus on pediatric lung transplantation. Significant variation was found in lung allocation systems worldwide both in terms of prioritization and distribution for children. Definition of pediatrics varied from <12 years of age to <18 years of age. While several countries performing LTX for young children do not have a formal system to prioritize pediatric candidates, many countries that perform LTX at higher rates do offer prioritization methods for children: including the United States, United Kingdom, France, Italy, Australia, and countries serviced by Eurotransplant. Certain lung allocation practices for pediatrics are highlighted herein, including the newly instituted Composite Allocation Score (CAS) system in the United States, pediatric matching with Eurotransplant, and pediatric prioritization in Spain. The systems highlighted here explicitly aim to provide judicious and high quality LTX care for children.

A theory of gender’s effect on vote shift with a test based on Turkish elections

ABSTRACT Despite great advances in the last decades, there is an enduring gender gap in a majority of the countries. Gender is prevalent in all aspects of life, and it is impossible to avoid gender roles. The choices regarding gender roles are threefold: Accept them, reject them, or have an ambivalent stand. When voters grow incongruous with their parties, they look for an alternative which is ideologically closer to them. However, evaluating this vote shift without the full effect of gender will provide an incomplete picture of electoral politics. I theorize that when voters are faced with female candidates or female party leaders, their assessment of gender roles will be a deciding factor for whether the intended vote shift will be realized. I apply this extended theory of ideological and gender role congruity to a high gender gap context, Turkey, utilizing district level electoral data. Taking into account the spatial dependence of factors that affect voting behaviour, I empirically demonstrate that for two ideologically opposing parties, the salience of gender politics through the ranking of female candidates on the closed-lists and presence of national female leadership affected their vote share in line with my theoretical expectations in the 2018 general election.