Screening Task Research Articles

Remote Screening for Developmental Language Disorder in Bilingual Children: Preliminary Validation in Spanish–Italian Speaking Preschool Children

Due to the difficulties in differentiating bilingual children with Developmental Language Disorder (DLD) from bilingual children with temporary language difficulties that may be caused by heterogeneous language input, language assessments of bilingual children are challenging for clinicians. Research demonstrates that assessments of bilingual children should be in all the languages a bilingual child speaks. This can be arduous for clinicians, but computerised screening approaches provide potential solutions. MuLiMi is a new web-based platform designed to automatise screening procedures for bilingual children at risk of DLD. To validate this procedure and investigate its reliability, 36 Spanish-speaking children, aged 4–6 years old, living in Italy, were tested remotely using the Italian–Spanish MuLiMi DLD screening. Sixteen of the participants were previously diagnosed with DLD. L2 (second or societal language) as well as L1 (first or family language) language abilities in static (nonword repetition, grammaticality judgement, and verb comprehension) as well as dynamic tasks (dynamic novel word learning) were assessed. Speed and accuracy of the children’s responses were automatically recorded (except nonword repetition). Significant associations emerged between the results obtained in the screening tasks when comparing them to parental questionnaires and standardised tests. An exploratory analysis of the diagnostic accuracy indicates that the single screening scores as well as the overall total score significantly contribute to DLD (risk) identification.

Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates.

Recently, commercial grade technologies have provided black box algorithms potentially relating to musculoskeletal injury (MSKI) risk and functional movement deficits, in which may add value to a high-performance model. Thus, the purpose of this manuscript was to evaluate composite and component scores from commercial grade technologies associations to MSKI risk in Marine Officer Candidates. 689 candidates (Male candidates = 566, Female candidates = 123) performed counter movement jumps on SPARTA™ force plates and functional movements (squats, jumps, lunges) in DARI™ markerless motion capture at the start of Officer Candidates School (OCS). De-identified MSKI data was acquired from internal OCS reports for those who presented to the Physical Therapy department for MSKI treatment during the 10weeks of training. Logistic regression analyses were conducted to validate the utility of the composite scores and supervised machine learning algorithms were deployed to create a population specific model on the normalized component variables in SPARTA™ and DARI™. Common MSKI risk factors (cMSKI) such as older age, slower run times, and females were associated with greater MSKI risk. Composite scores were significantly associated with MSKI, although the area under the curve (AUC) demonstrated poor discrimination (AUC = .55-.57). When supervised machine learning algorithms were trained on the normalized component variables and cMSKI variables, the overall training models performed well, but when the training models were tested on the testing data the models classified MSKI "by chance" (testing AUC avg = .55-.57) across all models. Composite scores and component population specific models were poor predictors of MSKI in candidates. While cMSKI, SPARTA™, and DARI™ models performed similarly, this study does not dismiss the use of commercial technologies but questions the utility of a singular screening task to predict MSKI over 10weeks. Further investigations should evaluate occupation specific screening, serial measurements, and/or load exposure for creating MSKI risk models.

The Future of INCOG (Is Now).

The Future of INCOG (Is Now).

MiDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.

The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages. Furthermore, we proposed a strategy combing both active learning and ensemble learning to improve the quality of the models. The models achieved satisfactory performance in the internal test datasets and four self-collected external test datasets. We also employed the models as a general index to make an evaluation on a widely known benchmark dataset DEKOIS 2.0, and surprisingly found a powerful ability on virtual screening tasks. Our model system (termed as miDruglikeness) provides a comprehensive drug-likeness prediction tool for drug discovery and development.

A Deep Learning approach for automated Cytoplasm and Nuclei cervical segmentation

Cervical cancer is a severe disease that affects many women in developing countries. Increasing screening capabilities is the best tool to reduce cancer incidence and save lives. Segmentation is an essential task in screening because it can lead to a better understanding of the morphological characteristics of cells. This paper presents a method for multi-class cell segmentation into Nuclei and Cytoplasm regions. Using the Herlev dataset for evaluation, this work achieved good performance by using state-of-the-art classification architectures such as EfficientNet combined with Feature Pyramid Networks to complete the segmentation task. Performance metrics for both classes show that the approach is robust enough to complete the task. The model achieved a 0.91 F1 score, 0.85 IoU, 0.91 Precision, 0.92 Recall, and 0.96 Specificity as class average with a very low standard deviation, validated with a 5-fold cross-validation. The proposal can help experts correctly asset cervical cell lesions and provide better healthcare for the patients.

Angular contrastive distillation driven self-supervised scanner independent screening and grading of retinopathy

Retinopathy is a group of retinal diseases that causes severe retinal damage, resulting in partial visual impairment or complete vision loss. Due to the capability of optical coherence tomography in revealing early retinal abnormalities, many researchers have utilized it to develop autonomous retinal screening systems. However, to the best of our knowledge, the majority of these systems are vulnerable against diversified scanner specifications, hence, requiring extensive training supervision (on large-scale datasets) to accurately learn retinal screening tasks. In this paper, we present a novel self-supervised segmentation-driven classification pipeline that employs a proposed angular contrastive distillation scheme to extract retinal lesions (from the multi-vendor data) in order to give lesion-aware scanner independent screening and grading of retinopathy. The diagnostic capacity of the proposed framework is further enhanced by the integration of a novel co-attention mechanism, which enables the underlying network to focus its attention on retinal abnormalities to effectively grade retinal diseases without incurring supervision from the ground truth labels. The proposed framework is rigorously validated on seven public datasets, acquired with four different scanners, where it outperforms its competitors by achieving 9.22% improvement in terms of mean intersection-over-union for extracting retinal lesions and 10.71% improvement in terms of F1 score for grading retinopathy.

Evaluation of a Multifaceted Protocol in Reducing Unnecessary Telemetry Monitoring across a Large Healthcare System.

Telemetry is frequently overused in hospitals. The goal of this study was to evaluate a telemetry protocol aimed at decreasing inappropriate telemetry utilization across four different hospitals within a large healthcare system by modifying the electronic telemetry order to incorporate the 2017 American Heart Association practice guidelines on the appropriate use of telemetry and using an electronic nursing screening task form to safely discontinue telemetry. We performed a retrospective analysis of telemetry utilization before and after we implemented a protocol across four hospitals within a large healthcare system. We compared the average number of days of telemetry monitoring and hospital length of stay during the preintervention period with the 6-month postintervention period. There were a total of 23,774 encounters evaluated. There was a statistically and clinically significant 24% decrease in telemetry duration between pre- and postintervention time periods (P < 0.0001). The mean (standard error) telemetry duration was 4.11 (0.17) and 2.36 (0.13) days in pre- and postintervention periods, respectively. The results of our study demonstrate a statistically significant decrease in overall duration of telemetry monitoring by nearly 1.75 days across each of the four hospitals with the implementation of a multifaceted telemetry protocol that included hardwiring the American Heart Association practice guidelines into the electronic order and using a nursing-driven discontinuation protocol.

Conversion Between the Mini-Mental State Examination and the Montreal Cognitive Assessment for Patients With Different Forms of Dementia.

The Mini-Mental Status Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are 2 frequently used brief cognitive screening tasks. Here, we provide a conversion method from MMSE to MoCA for patients with Alzheimer's dementia, frontotemporal dementia, and Parkinson dementia/Lewy body dementia, as well as for patients with dementia and with or without previous stroke. This conversion is needed as everyday clinical practice varies in their use of the 2 scales, which makes comparisons between studies, meta-analysis, and patient cohorts difficult. Observational cohort study. A total of 387 patients with recently diagnosed dementia in memory clinics from the Swedish registry for cognitive/dementia disorders (SveDem) from 2007 to2018. Overall, 387 patients of the Swedish registry for cognitive/dementia disorders with both MMSE and MoCA scores were evaluated. An equipercentile equating method was used to convert MMSE to MoCA scores in the different patient populations. Furthermore, receiver operating curves were used to examine whether MMSE or MoCA scores can distinguish between patients with different dementia types. MMSE scores were converted to MoCA scores for all dementia types and depicted in a conversion table. Results show that the equipercentile equating method and log-linear smoothing allow the creation of a conversion table in which for each test score of the MMSE, the equivalent score of the MoCA for each investigated group can be looked up (and vice-versa). This study reports a reliable and easy conversion for transforming MMSE to MoCA scores (and vice-versa) in patients with Alzheimer's dementia, frontotemporal dementia, Parkinson dementia or Lewy body dementia, as well as patients with dementia with and without previous stroke.

A few-shot learning-based eye diseases screening method.

This study aims to construct a brand-new ophthalmic disease screening task and establish a practically valuable ophthalmic disease screening model in the case of insufficient data. The main methods are as follows: firstly, we mixed data from different sources (these data may come from different cameras, including different fundus diseases) to get a new dataset. Based on this dataset, we conducted subsequent experiments on fundus multi-disease screening. However, in the past public datasets, each dataset often only corresponded to the screening diagnosis of one disease. Secondly, we proposed a method to simulate the characteristics of different fundus cameras by using a method based on style transfer, and to augment the training data, so that the model could learn the features of ophthalmic diseases in a more comprehensive way. Finally, a robust disease screening model based on few-shot learning was constructed on the combined dataset, and compared with benchmark algorithms. We focused on the study of eye disease screening methods based on the metric-based few-shot learning model, data augmentation methods, and focus on key technologies such as data augmentation based on style transfer. Experiments have shown that our method can significantly improve the generalization ability of the disease screening model. By introducing few-shot learning theory and data augmentation based on style transfer into ophthalmic disease screening, the generalization ability of the model is greatly improved, and it has certain practical value.

FSDscore: An Effective Target-focused Scoring Criterion for Virtual Screening.

Improving screening efficiency is one of the most challenging tasks of virtual screening (VS). In this work, we propose an effective target-focused scoring criterion for VS and apply it to the screening of a specific target scaffold replacement library constructed by enumeration of suitable substitution fragments and R-groups of known ligands. This criterion is based on both ligand- and structure-based scoring methods, which includes feature maps, 3D shape similarity, and the pairwise distance information between proteins and ligands (FSDscore). It is precisely due to the hybrid advantages of ligand- and structure-based approaches that FSDscore performs far better on the validation dataset than other scoring methods. We apply FSDscore to the VS of different kinase targets, MERTK (Mer tyrosine kinase) and ABL1 (tyrosine-protein kinase ABL1) in order to avoid occasionality. Finally, a VS case study shows the potential and effectiveness of our scoring criterion in drug discovery and molecular dynamics simulation further verifies its powerful ability.

Inter-Rater and Intra-Rater Reliability of Return-to-Work Screening Tests for UK Firefighters Following Injury.

The aim of this study was to assess the inter-rater and intra-rater reliability of a return-to-work (RTW) screening test to be used on UK firefighters following injury. The inter rater and intra-rater reliability of eight tasks involved in a screening test was used to assess readiness to RTW for UK firefighters following injury. These tasks included the following: (1) putting on and removing a breathing apparatus set (BA), (2) a ladder lift simulation, (3) a ladder carry simulation, (4) a light portable pump (LPP) lift and carry simulation, (5) a hose run, (6) a ladder climb with leg lock, (7) a casualty evacuation and (8) a confined space crawl simulation. The inter-rater reliability between each individual screening task was interpreted as Excellent (ICC = 0.94-1.00) for eleven (68.75%) of the screening task videos and as Good (ICC = 0.75-0.88) for five (31.25%) of the screening task videos. Intra-rater reliability was interpreted as Excellent (ICC = 1) for twenty-six participants (74.3%), Good (ICC = 0.76-0.88) for eight participants (22.9%) and Moderate for one participant (2.8%). Due to the reliability of this screening test, it allows conclusions to be made from the results which can inform a RTW decision for a firefighter.

Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program.

Background: Artificial intelligence (AI) has been successfully applied to the screening tasks of fundus diseases. However, few studies focused on the potential of AI to aid medical teaching in the residency training program. This study aimed to evaluate the effectiveness of the AI-based pathologic myopia (PM) identification system in the ophthalmology residency training program and assess the residents' feedback on this system. Materials and Methods: Ninety residents in the ophthalmology department at the Second Affiliated Hospital of Zhejiang University were randomly assigned to three groups. In group A, residents learned PM through an AI-based PM identification system. In group B and group C, residents learned PM through a traditional lecture given by two senior specialists independently. The improvement in resident performance was evaluated by comparing the pre-and post-lecture scores of a specifically designed test using a paired t-test. The difference among the three groups was evaluated by one-way ANOVA. Residents' evaluations of the AI-based PM identification system were measured by a 17-item questionnaire. Results: The post-lecture scores were significantly higher than the pre-lecture scores in group A (p < 0.0001). However, there was no difference between pre-and post-lecture scores in group B (p = 0.628) and group C (p = 0.158). Overall, all participants were satisfied and agreed that the AI-based PM identification system was effective and helpful to acquire PM identification, myopic maculopathy (MM) classification, and "Plus" lesion localization. Conclusion: It is still difficult for ophthalmic residents to promptly grasp the knowledge of identification of PM through a single traditional lecture, while the AI-based PM identification system effectively improved residents' performance in PM identification and received satisfactory feedback from residents. The application of the AI-based PM identification system showed advantages in promoting the efficiency of the ophthalmology residency training program.

Evaluating the hearing screening effectiveness of active noise cancellation technology among young adults: A pilot study.

The population of young adults who are hearing impaired increases yearly, and a device that enables convenient hearing screening could help monitor their hearing. However, background noise is a critical issue that limits the capabilities of such a device. Therefore, this study evaluated the effectiveness of commercial active noise cancellation (ANC) headphones for hearing screening applications in the presence of background noise. In particular, six confounders were used for a comprehensive evaluation. We enrolled 12 young adults (a total of 23 ears with normal hearing) to participate in this study. A cross-sectional self-controlled study was conducted to explore the effectiveness of hearing screening in the presence of background noise, with a total of 240 test conditions (=3 ANC models × 2 ANC function statuses × 2 noise types × 5 noise levels × 4 frequencies) for each test ear. Subsequently, a linear regression model was used to prove the effectiveness of ANC headphones for hearing screening applications in the presence of background noise with six confounders. The experimental results showed that, on average, the ANC function of headphones can improve the effectiveness of hearing screening tasks in the presence of background noise. Specifically, the statistical analysis showed that the ANC function enabled a significant 10% improvement ( p < 0.001) compared with no ANC function. This study confirmed the effectiveness of ANC headphones for young adult hearing screening applications in the presence of background noise. Furthermore, the statistical results confirmed that as confounding variables, noise type, noise level, hearing screening frequency, ANC headphone model, and sex all affect the effectiveness of the ANC function. These findings suggest that ANC is a potential means of helping users obtain high-accuracy hearing screening results in the presence of background noise. Moreover, we present possible directions of development for ANC headphones in future studies.

Clinical and Metabolic Signature of UNC13A rs12608932 Variant in Amyotrophic Lateral Sclerosis.

Background and ObjectivesTo characterize the clinical and cognitive behavioral phenotype and brain 18F-2-fluoro-2-deoxy-d-glucose-PET (18F-FDG-PET) metabolism of patients with amyotrophic lateral sclerosis (ALS) carrying the rs12608932 variant of the UNC13A gene.MethodsThe study population included 1,409 patients with ALS without C9orf72, SOD1, TARDBP, and FUS mutations identified through a prospective epidemiologic ALS register. Control participants included 1,012 geographically matched, age-matched, and sex-matched participants. Clinical and cognitive differences between patients carrying the C/C rs12608932 genotype and those carrying the A/A + A/C genotype were assessed. A subset of patients underwent 18F-FDG-PET.ResultsThe C/C genotype was associated with an increased risk of ALS (odds ratio: 1.54, 95% confidence interval 1.18–2.01, p = 0.001). Patients with the C/C genotype were older, had more frequent bulbar onset, and manifested a higher rate of weight loss. In addition, they showed significantly reduced performance in the letter fluency test, fluency domain of Edinburgh Cognitive and Behavioural ALS Screen (ECAS) and story-based empathy task (reflecting social cognition). Patients with the C/C genotype had a shorter survival (median survival time, C/C 2.25 years, interquartile range [IQR] 1.33–3.92; A/A + C/C: 2.90 years, IQR 1.74–5.41; p = 0.0001). In Cox multivariable analysis, C/C genotype resulted to be an independent prognostic factor. Finally, patients with a C/C genotype had a specific pattern of hypometabolism on brain 18F-FDG-PET extending to frontal and precentral areas of the right hemisphere.DiscussionC/C rs12608932 genotype of UNC13A is associated with a specific motor and cognitive/behavioral phenotype, which reflects on 18F-FDG-PET findings. Our observations highlight the importance of adding the rs12608932 variant in UNC13A to the ALS genetic panel to refine the individual prognostic prediction and reduce heterogeneity in clinical trials.

CSF α-Synuclein and Tau as Biomarkers for Dementia With Lewy Bodies: A Systematic Review and Meta-analysis.

This study investigated whether α-synuclein and tau in cerebrospinal fluid (CSF) can be used as biomarkers to diagnose dementia with Lewy bodies (DLB). We retrieved 3303 studies with "Dementia with Lewy bodies," "α-synuclein," and "tau" as keywords. We formulated screening criteria, and 2 researchers completed the screening, quality evaluation, and data extraction tasks. Finally, 35 studies related to tau, and 14 studies related to α-synuclein were included. Review Manager 5.4 and Stata16 were used for meta-analysis. Subgroup, sensitivity, and meta-regression analyses were performed to identify sources of heterogeneity and strengthen the results. Compared with the control group, DLB patients showed significantly higher CSF levels of tau [weighted mean difference=81.36 (59.82, 102.91); Z =7.40; P <0.00001], and lower CSF levels of α-synuclein [weighted mean difference=-95.25 (-162.02, -28.48); Z =2.80; P =0.005]. Mini-Mental State Examination (MMSE) score, male ratio, and disease duration were not sources of heterogeneity on subgroup and meta-regression analyses. Sensitivity analysis revealed no significant differences. Higher levels of tau and lower levels of α-synuclein were found in the CSF of patients with DLB compared with the control group. Therefore, CSF tau and α-synuclein levels may be diagnostic biomarkers for DLB.

Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning.

Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.

Justice and risk assessment: the subjectivity of pre-emptive screening of radicalisation processes

ABSTRACT In recent years, the fight against (violent) extremism has focused more on anticipating the threats that they pose. Therefore, early detection of undemocratic ideas by local professionals has become an important part of the preventive approach in counter terrorism radicalisation. Frontline workers who operate in the arteries of society are encouraged to identify processes toward violent behaviour at an early stage. To date, however, little is known about how these professionals take on this screening task at their own discretion. The analysis of 55 interviews with youth workers, municipality civil servants, and community police officers, show that they tended to be insufficiently equipped in general to detect radicalisation towards (violent) extremism at the local level in the Netherlands. Firstly, this is due to varying contents and qualities of training courses which are not suited to building up solid expertise. Secondly, and most importantly, the recognising of deviant behaviour is presumably carried out with a one-sided focus on personal norms and values rather than structured judgements about pathways towards risky behaviour. Various value systems seem to influence the norm for early detection, which means that there is, in practice, a lack of clear indicators.

Quantification Of Limb Loading Asymmetries In Collegiate Basketball Players During Basketball Tasks

Lower extremity biomechanical asymmetries have been previously identified in basketball athletes during individual screening tasks. The amount of asymmetry that occurs during basketball-specific tasks is not as well understood. PURPOSE: To identify the extent of lower limb loading asymmetry, as measured by a wearable sensor, that occurs during basketball-specific tasks and to determine whether sex-differences in loading patterns exist between male and female basketball athletes. METHODS: Fifteen NCAA Division-I men’s (n: 7, height: 187.3 ± 15.2 cm, mass: 91.4 ± 20.9 kg) and women’s (n: 8, height: 175.5 ± 10.9 cm, mass: 73.8 ± 11.6 kg) basketball athletes participated in the study. Each participant wore an inertial measurement unit on the distal medial aspect of each tibia while completing a series of basketball-related tasks (i.e. drop vertical jump, single leg countermovement jump), and eight basketball-specific tasks (e.g. dribbling, shooting, cutting, sprinting) over the course of a 30-minute session. The average intensity of each impact as measured by tibial acceleration and impact load as a function of the number and intensity of impacts were used for analysis. 2 (side) x 2 (gender) repeated measures ANOVA with post-hoc independent t-tests were used to identify side-to-side asymmetries and sex differences in intensity and loading variables (α = 0.05). RESULTS: For all participants, there were no significant differences in average loading intensity (dominant limb (D): 10.4 ± 1.5 g, non-dominant limb (ND): 10.1 ± 1.5 g) or impact load (D: 6007 ± 1245 g, ND: 5720 ± 1033 g) between sides (p > 0.05). There were significant sex differences in the average intensity of loading such that female basketball players exhibited greater average loading than males in the dominant (M: 9.5 ± 1.0 g, F: 11.1 ± 1.5 g; p = 0.04) and non-dominant limb (M: 9.0 ± 0.8 g, F: 11.1 ± 1.4 g; p = 0.005). CONCLUSIONS: No significant loading asymmetries were identified in collegiate basketball players, warranting further study in a natural, non-simulated environment. Significant differences in loading were identified such that female basketball players had higher average loading than male basketball players, potentially due to differences in force attenuation strategies during running, cutting, jumping and landing.

Benefits of Decision Support Systems in Relation to Task Difficulty in Airport Security X-Ray Screening

Automated explosive detection systems for cabin baggage screening (EDSCB) highlight areas in X-ray images of passenger bags that could contain explosive material. Several countries have implemented EDSCB so that passengers can leave personal electronic devices in their cabin baggage. This increases checkpoint efficiency, but also task difficulty for screeners. We used this case to investigate whether the benefits of decision support systems depend on task difficulty. 100 professional screeners conducted a simulated baggage screening task. They had to detect prohibited articles built into personal electronic devices that were screened either separately (low task difficulty) or inside baggage (high task difficulty). Results showed that EDSCB increased the detection of bombs built into personal electronic devices when screened separately. When electronics were left inside the baggage, operators ignored many EDSCB alarms, and many bombs were missed. Moreover, screeners missed most unalarmed explosives because they over-relied on the EDSCB’s judgment. We recommend that when EDSCB indicates that the bag might contain an explosive, baggage should always be examined further in a secondary search using explosive trace detection, manual opening of bags and other means.

The Effects of Religion on Social Media Assessments and Hiring

Religion has become an increasingly divisive issue in European countries and in the United States following terrorist attacks and waves of immigration. At the same time, social media has made accessing individuals’ personal religious information more easily available than ever. Building on the handful of studies that have examined religious discrimination in hiring, we draw on research on stereotypes, identification, and similarity-attraction to develop and test a mediated model of religious discrimination in hiring. Further, as prior research has largely relied on Christian respondents, we tested our model with a stratified sample of Jewish and Christian raters, to determine whether prior results favoring Christian candidates represent a pro-Christian sentiment or a similarity effect. We designed an experiment using a social media screening task to test our hypotheses. Our results indicate that in the combined sample, religion did not affect expected task or OCB performance; however, in the Christian-only sample, religion had a marginally significant effect on OCB performance. Further, within each subsample, raters identified more strongly with applicants of their own religion than with applicants of other religions and in the Jewish sample, we also found that raters expressed less disidentification with Jewish applicants. In turn, these variables were associated with liking, warmth and competence, and ultimately, task and OCB performance.