Detection Bias Research Articles

Modifiable risk factors for subsequent lethal prostate cancer among men with an initially negative prostate biopsy.

Previously suggested modifiable risk factors for prostate cancer could have resulted from detection bias because diagnosis requires a biopsy. We investigated modifiable risk factors for a subsequent cancer diagnosis among men with an initially negative prostate biopsy. In total, 10,396 participants of the Health Professionals Follow-up Study with an initial negative prostate biopsy after 1994 were followed for incident prostate cancer until 2017. Potential risk factors were based on previous studies in the general population. Outcomes included localised, advanced, and lethal prostate cancer. With 1851 prostate cancer cases (168 lethal) diagnosed over 23 years of follow-up, the 20-year risk of any prostate cancer diagnosis was 18.5% (95% CI: 17.7-19.3). Higher BMI and lower alcohol intake tended to be associated with lower rates of localised disease. Coffee, lycopene intake and statin use tended to be associated with lower rates of lethal prostate cancer. Results for other risk factors were less precise but compatible with and of similar direction as for men in the overall cohort. Risk factors for future prostate cancer among men with a negative biopsy were generally consistent with those for the general population, supporting their validity given reduced detection bias, and could be actionable, if confirmed.

Atopic dermatitis and cognitive dysfunction in middle-aged and older adults: A systematic review and meta-analysis.

Atopic dermatitis (AD) is a common chronic inflammatory skin disease that affects adults worldwide. Recent evidence suggests that AD may be associated with cognitive dysfunction, but the results of individual studies have been inconsistent. This systematic review and meta-analysis aimed to evaluate the association between AD and cognitive dysfunction in middle-aged and older adults. To find relevant research, a comprehensive search of electronic databases from the beginning to March 2023 was carried out. Data were taken from studies that were eligible, and a meta-analysis was done to determine the pooled hazard ratio (HR) and 95% confidence interval (CI). We searched three databases and found a total of 15 studied arms included in 5 cohort studies with over 8.5 million participants were included in the analysis. The results showed that individuals with AD had a higher risk of developing dementia of all-cause dementia (pooled hazard ratio (HR) = 1.16; 95% CI, 1.10-1.23,P<0.001) and the Alzheimer type (pooled HR = 1.28; 95% CI, 1.01-1.63,P<0.001) but not vascular dementia (pooled HR = 1.42; 95% CI, 0.99-2.04,P<0.001). Subgroup analyses showed that the association between atopic dermatitis and all-cause dementia was significant in Europe (P = 0.004) but not in Asia (P = 0.173) and was significant in prospective cohort studies (P<0.001) but not in non-prospective cohort studies (P = 0.068). Sensitivity analysis and publication bias detection confirmed the reliability of the overall findings. In conclusion, this study demonstrated that AD was associated with increased risk of cognitive dysfunction, particularly dementia of the Alzheimer type and all-cause dementia, in middle-aged and older participants. Further research is needed to understand the mechanisms behind this association and its potential implications for clinical practice. PROSPERO, identifier (CRD42023411627).

GOES GLM, biased bolides, and debiased distributions

The large combined field of view of the Geostationary Lightning Mapper (GLM) instruments onboard the GOES weather satellites makes them useful for studying the population of other atmospheric phenomena, such as bolides. Being a lightning mapper, GLM has many detection biases when applied to non-lightning and these systematics must be studied and properly accounted for before precise measurements of bolide flux can be ascertained. We developed a Bayesian Poisson regression model which simultaneously estimates instrumental biases and our statistic of principal interest: the latitudinal variation of bolide flux. We find that the estimated bias due to the angle of incident light upon the instrument corresponds roughly with the known sensitivity of the GLM instruments. We compare our latitudinal flux variation estimates to existing theoretical models and find our estimates consistent with GLM being strongly biased towards high-velocity bolides.

ChatGPT v.s. media bias: A comparative study of GPT-3.5 and fine-tuned language models

In our rapidly evolving digital sphere, the ability to discern media bias becomes crucial as it can shape public sentiment and influence pivotal decisions. The advent of large language models (LLMs), such as ChatGPT, noted for their broad utility in various natural language processing (NLP) tasks, invites exploration of their efficacy in media bias detection. Can ChatGPT detect media bias? This study seeks to answer this question by leveraging the Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in distinguishing six categories of media bias, juxtaposed against fine-tuned models such as Bidirectional and Auto-Regressive Transformers (BART), Convolutional Bidirectional Encoder Representations from Transformers (ConvBERT), and Generative Pre-trained Transformer 2 (GPT-2). The findings present a dichotomy: ChatGPT performs at par with fine-tuned models in detecting hate speech and text-level context bias, yet faces difficulties with subtler elements of other bias detections, namely, fake news, racial, gender, and cognitive biases.

Synbiotics, prebiotics and probiotics for people with chronic kidney disease.

Synbiotics, prebiotics and probiotics for people with chronic kidney disease.

The State of Use and Utility of Negative Controls in Pharmacoepidemiologic Studies.

Uses of real-world data in drug safety and effectiveness studies are often challenged by various sources of bias. We undertook a systematic search of the published literature through September 2020 to evaluate the state of use and utility of negative controls to address bias in pharmacoepidemiologic studies. Two reviewers independently evaluated study eligibility and abstracted data. Our search identified 184 eligible studies for inclusion. Cohort studies (115, 63%) and administrative data (114, 62%) were, respectively, the most common study design and data type used. Most studies used negative control outcomes (91, 50%), and for most studies the target source of bias was unmeasured confounding (93, 51%). We identified 4 utility domains of negative controls: 1) bias detection (149, 81%), 2) bias correction (16, 9%), 3) P-value calibration (8, 4%), and 4) performance assessment of different methods used in drug safety studies (31, 17%). The most popular methodologies used were the 95% confidence interval and P-value calibration. In addition, we identified 2 reference sets with structured steps to check the causality assumption of the negative control. While negative controls are powerful tools in bias detection, we found many studies lacked checking the underlying assumptions. This article is part of a Special Collection on Pharmacoepidemiology.

Psilocybin, an Effective Treatment for Major Depressive Disorder in Adults - A Systematic Review.

Psilocybin is a classical psychedelic which has been utilised for healing purposes for millenia. However, with its classification as a Schedule I substance, research into this compound is scarce with few FDA-approved clinical studies. Despite this, profound findings into its antidepressant effects (largely through its action on 5-HT1a receptors) in mental illnesses such as major depressive disorder have rapidly increased interest back into their potential therapeutic benefits. This systematic review provides an analysis of the studies examining the clinical use of psilocybin for major depressive disorder. In total 6 studies were selected, including 319 participants, with half being randomised controlled trials and half open label trials. In every study psychological support was included as an integral part of the treatment. It was found that every study significantly favoured the use of psilocybin in reducing depressive symptoms, with few side effects. This gives psilocybin an advantage over commonly prescribed selective serotonin reuptake inhibitors (SSRIs), which carry more risk and cause more adverse effects. This drug therefore shows promise for being used as a clinical treatment for major depressive disorder, however future research should develop a paradigm for its use, with the timing of sessions and type of psychological support specified to allow for more precise analysis of the clinical effects of the drug. Additionally, more studies into its clinical efficacy are needed for appropriate detection of any publication bias. With this, psilocybin could prove to be revolutionary in treating depression and become an alternative medication to SSRIs.

Influence of bystander activation on pre-hospital emergency care response time: systematic review

This article presents a systematic review and analysis of grey literature to identify and address gaps in knowledge regarding the role and influence of bystander activation on pre-hospital emergency care (PEC) response time. We conducted a systematic search for full-text articles published since 2000 in Web of Science, PubMed, Science Direct, and Google Scholar databases. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, using “pre-hospital emergency care response time” and “bystanders” as search keywords. The risk of bias was assessed using the ROBINS-I tool. Our analysis included forty-six relevant studies meeting the inclusion criteria. However, we observed that many studies were poorly reported, posing risks of selection and detection biases. Additionally, we identified methodological and study design weaknesses in five studies. Given the critical role of PEC services in saving lives and preventing medical complications, the timely provision of these services is paramount. Bystanders play a central role in activating emergency medical services (EMS) and providing cardiopulmonary resuscitation. Prompt calls to EMS by bystanders resulted in reduced PEC response times, improved survival chances, and better neurological outcomes, particularly among out-of-hospital cardiac arrest patients. There is substantial evidence that prompt bystander activation of EMS significantly reduces PEC response times, thereby saving lives and strengthening existing PEC systems. However, further research is necessary to accurately assess the impact of different interventions aimed at enhancing bystander activation of EMS and reducing PEC response times.

MetaSVs: A pipeline combining long and short reads for analysis and visualization of structural variants in metagenomes.

Structural variants (SVs, including large-scale insertions, deletions, inversions, and translocations) significantly impact the functions of genes in the microbial genome, and SVs in the microbiome are associated with diverse biological processes and human diseases. With the advancements in sequencing and bioinformatics technologies, increasingly, sequencing data and analysis tools are already being extensively utilized for microbiome SV analyses, leading to a higher demand for more dedicated SV analysis workflows. Moreover, due to the unique detection biases of various sequencing technologies, including short-read sequencing (such as Illumina platforms) and long-read sequencing (e.g., Oxford Nanopore and PacBio), SV discovery based on multiple platforms is necessary to comprehensively identify the wide variety of SVs. Here, we establish an integrated pipeline MetaSVs combining Nanopore long reads and Illumina short reads to analyze SVs in the microbial genomes from gut microbiome and further identify differential SVs that can be reflective of metabolic differences. Our pipeline provides researchers easy access to SVs and relevant metabolites in the microbial genomes without the requirement of specific technical expertise, which is particularly useful to researchers interested in metagenomic SVs but lacking sophisticated bioinformatic knowledge.

Undergraduate research implementation in physiotherapy: a hands-on and real experience of a randomised controlled trial

BackgroundEvidence-based practice (EBP) is the gold standard approach in physiotherapy, and it is essential that students are aware that it is the appropriate way to provide the patient with the best possible treatment. Undergraduate research (UR) can positively influence learning outcomes and research competencies related to EBP compared to traditional methods of higher education. The aim of this study was to evaluate the effect of implementing a research-based activity (i.e., active participation in a randomised controlled trial [RCT]) in the UR programme on the learning and acquisition of research methodology-related competencies by first-year physiotherapy students.MethodsStudents in the first academic year of the Bachelor´s Degree in Physiotherapy of University of Deusto (Donostia-San Sebastian, Spain) who were enrolled in the subject ‘Introduction to Research Methodology’ were invited to take part in a real RCT which consisted of three groups: intervention, placebo, and control group. While the RCT was carried out, researchers and/or participants roles were combined among students during the semester. At the end, a questionnaire that included open and closed questions was used to evaluate the effectiveness of the UR strategies used in students´ acquisition of theoretical knowledge, research competencies, self-efficacy of RCT skills and procedures. Lecturers also completed the questionnaire to evaluate their experience.ResultsFrom the 114 students enrolled in the subject, 102 participated in the RCT and 110 answered the final questionnaire. Regarding the development of research competencies, UR had a positive or very positive impact on critical thinking (67% and 18%, respectively) and in the assessment of methodological quality (66% and 23%, respectively). Furthermore, most students reported that the implementation of the RCT facilitated their knowledge of placebo, detection of bias, development of critical thinking and a better understanding of methodological issues in research. Lecturers reported an additional burden that was difficult to reconcile with daily duties.ConclusionThe novel UR program provided students with a new opportunity to improve their knowledge of RCT procedures, thus making the learning process more meaningful. Therefore, ways of teaching and learning focused on improving research and inquiry attitudes should be considered and integrated into the health care curriculum, especially in physiotherapy programs, to ensure the transfer of EBP for the provision of the best care.Trial registrationAustralian New Zealand Clinical Registry: ACTRN12622000263796p (14/02/2022).

Bias detection and knowledge graph comparison analysis for medical record datasets

Abstract In this paper, firstly, we study the reinforcement learning algorithm, which is a related technology of knowledge graph, and use reinforcement learning to structure the application framework of health care knowledge graph and construct the domain dictionary. And based on the BitLab21 dataset, entity relationship annotation is performed on the named entity recognition result set to realize entity relationship extraction of clinical electronic medical records. Then, the HacRED dataset is used to evaluate the deep learning model, and comparison tests and ablation experiments are conducted to verify the effectiveness of reinforcement learning for constructing knowledge graphs, respectively. Finally, a deviation monitoring method and a data feature extraction method for the case dataset are proposed, and a comparative study of different feature extraction methods is conducted to illustrate the advantages of feature fusion in distinguishing abnormal deviation patterns. All three features have relatively good recognition accuracy for normal patterns, which can reach 100%. The error curves perform well, and their training and testing errors converge very quickly to below 0.01 with less than 5 iterations and achieve nearly 100% recognition accuracy. Reinforcement learning-based knowledge mapping of case datasets and feature fusion-based data machine bias detection can improve the efficiency of medical detection.

Modified maximum likelihood space registration method for shipborne multi-radar signal processing

Aiming at the spatial registration problem for networking of multi-platform shipborne radars, a modified maximum likelihood (MML) absolute registration algorithm based on singular value decomposition (SVD) is proposed. Firstly, the four major biases (radar detection biases, platform positioning biases, attitude biases, and baseline conversion biases) which affect shipborne radar detection are analyzed, and an equivalent model of shipborne radar detection biases is established to make the bias parameters more efficiently. Secondly, the maximum likelihood registration method is improved. According to the singular value decomposition, the conditional index is used to evaluate the observability in the maximum likelihood registration method in realtime, and the abnormal conditional index is corrected to effectively reduce the fluctuation of the bias estimation. Finally, the simulation results of two different scenarios are conducted, and the results show that the new method has significant improvements in estimation accuracy and algorithm stability compared with the existing algorithms.

Hybrid mark–resight approaches account for heterogeneous detection bias in aerial surveys

Abstract Population abundance estimation is a critical yet challenging task for wildlife management decision‐making. For aerial surveys of terrestrial mammals in highly heterogenous landscapes, estimation models that address multiple sources of detection bias are needed. A typical hybrid mark–resight model includes an additive parameter that accounts for detection probabilities derived from marked animals. While this additive hybrid method improves abundance estimates, there remains a need to account for heterogeneity due to the conditional dependence in detection probability inherent in aerial surveys with multiple observers. We propose a modified approach, referred to as a conditional hybrid model, to address this heterogeneity directly. The conditional hybrid model applies a mark–resight approach typically used to account for tag loss in mark–recapture studies to aerial surveys with a subset of the population marked with GPS collars. We conduct a structured comparison between the additive and conditional hybrid estimation models and fit these two hybrid models to data from a 2020 aerial survey of mule deer in southern California. Both the additive and conditional hybrid models had general agreement in overall model structure and variable importance, however, the conditional model outperformed the additive model in capturing sources of detection heterogeneity and overall abundance estimates differed by 9%. Our top‐ranked models suggest that deer movement, larger group sizes, and rougher topography increased detection probability, whereas high cloud cover, obscuring vegetation and high observer fatigue and helicopter height decreased detection probability. Our findings illustrate the need to assess multiple correction methods when estimating abundance from aerial survey data to ensure accurate abundance estimates are being used to inform wildlife management strategies, with the caveat that specific survey conditions may dictate which method is the most appropriate for achieving robust estimates. These approaches represent the cutting‐edge science on wildlife abundance estimation from aerial survey data, a critical ingredient for effective wildlife management. By testing innovative hybrid methods, we present the first formal comparison of the strengths and weaknesses of each method within a highly heterogeneous landscape, providing practitioners with necessary information for future application.

Cosmology inference at the field level from biased tracers in redshift-space

Cosmology inference of galaxy clustering at the field level with the EFT likelihood in principle allows for extracting all non-Gaussian information from quasi-linear scales, while robustly marginalizing over any astrophysical uncertainties. A pipeline in this spirit is implemented in the LEFTfield code, which we extend in this work to describe the clustering of galaxies in redshift space. Our main additions are: the computation of the velocity field in the LPT gravity model, the fully nonlinear displacement of the evolved, biased density field to redshift space, and a systematic expansion of velocity bias. We test the resulting analysis pipeline by applying it to synthetic data sets with a known ground truth at increasing complexity: mock data generated from the perturbative forward model itself, sub-sampled matter particles, and dark matter halos in N-body simulations. By fixing the initial-time density contrast to the ground truth, while varying the growth rate f, bias coefficients and noise amplitudes, we perform a stringent set of checks. These show that indeed a systematic higher-order expansion of the velocity bias is required to infer a growth rate consistent with the ground truth within errors. Applied to dark matter halos, our analysis yields unbiased constraints on f at the level of a few percent for a variety of halo masses at redshifts z = 0, 0.5, 1 and for a broad range of cutoff scales 0.08 h/Mpc≤ Λ ≤ 0.20 h/Mpc. Importantly, deviations between true and inferred growth rate exhibit the scaling with halo mass, redshift and cutoff that one expects based on the EFT of Large Scale Structure. Further, we obtain a robust detection of velocity bias through its effect on the redshift-space density field and are able to disentangle it from higher-derivative bias contributions.

Environmental DNA compliments traditional sampling for monitoring fish communities in a Texas estuary

AbstractObjectiveEnvironmental DNA (eDNA) metabarcoding has become an important method for inventorying and monitoring biota in aquatic systems. The Texas Parks and Wildlife Department conducts regular fishery‐independent sampling of biotic communities using traditional sampling gears, such as gill nets and bag seines, in all of the major estuaries of adjacent to the Gulf of Mexico in Texas. Previous studies have shown that eDNA approaches can complement traditional sampling methods.MethodsWe compared fish community structure data in the Cedar Lakes estuary system obtained with traditional sampling gears with data obtained using eDNA sampling using a small sequence of mitochondrial 12S ribosomal RNA gene and a validated taxonomic reference file.ResultFor spring and fall of 2022, eDNA metabarcoding detected a larger number of species than either bag seines or gill nets. Species richness detected via eDNA in two seasons in a single year was comparable with the species richness of agency’s historical record based on traditional gears for Cedar Lakes.ConclusionSeasonal and spatial variation in species richness was similar between traditional and eDNA sampling; however, eDNA metabarcoding allowed detection of several species that would be difficult or impossible to capture with either bag seines or gill nets. We observed two limitations of eDNA metabarcoding. Read depth was not a good index of relative abundance, which limits our ability to infer relative biomass using single samples. Secondly, we observed detection bias in our eDNA results. Specifically, eDNA failed to detect two species of elasmobranchs present when water sampling was performed and eDNA also performed poorly compared to traditional sampling gears for some species of bony fishes. Despite these limitations, eDNA metabarcoding proved to be an efficient and cost‐effective alternative and compliment to traditional fisheries sampling gears for fishery‐independent monitoring of community structure and composition in estuaries of the Gulf of Mexico.

Enhanced Direct-Current Bias Detection Method Based on AC-Modulated Tunneling Magnetoresistive Sensor for Transformer-Based Renewable Energy Systems

Enhanced Direct-Current Bias Detection Method Based on AC-Modulated Tunneling Magnetoresistive Sensor for Transformer-Based Renewable Energy Systems

Bayesian Detection of Bias in Peremptory Challenges Using Historical Strike Data

United States law bars using peremptory strikes during jury selection because of prospective juror race, ethnicity, sex, or membership in certain other cognizable classes. Here, we extend a Bayesian approach for detecting such illegal strike bias by showing how to incorporate historical data on an attorney’s use of peremptory strikes in past cases. In so doing, we use the power prior to adjust the weight of such historical information in the analysis. Using simulations, we show how the choice of the power prior’s discounting parameter influences bias detection (how likely the credible interval for the bias parameter excludes zero), depending on the degree of incompatibility between current and historical trial data. Finally, we extend this approach with a prototype software application that lawyers could use to detect strike bias in real time during jury-selection. We illustrate this application’s use with real historical strike data from a convenience sample of cases from one court.

The Pedagogical Value of Museums in the Teaching and Learning of Secondary School History: A Historical Thinking Perspective

Using historical thinking for analyzing the teaching and learning of secondary school history, this paper contributes to literature and debates on the pedagogical potential of museums in this endeavor. Despite the existence of museums and expansive literature on their historical significance in various world settings, there has not been much effort to connect history teaching and learning to this institution. Drawing our arguments from literature on historical thinking and museum studies, we explore the value of museums in the teaching and learning of secondary school history. We commend the inherent value in museums if their educational programs are packaged effectively for this purpose. Apart from connecting learners to cognitive activities as well as fostering an understanding and application of its disciplinary competencies, museums also engage with elements of historical thinking. Disciplinary competencies of history include critical thinking, extrapolation, evaluation of sources and detection of bias. Elements of historical thinking which makes museums stand out as potentially significant in secondary school pedagogy are empathy, historical significance, primary source evidence, cause and consequence and continuity and change. However, despite their esteemed pedagogical value, operationalizing teaching and learning of history using various museums facilities comes with several challenges which include high cost of organizing lessons, a strain on the school timetable, complexities associated with logistical and legal demands of out of school visits. Nonetheless, several innovations can be used to deal with such challenges.

Thermal Challenges in Dynamic Infrared Thermography Used for Perforator Mapping.

The aim of this study is to investigate the different approaches to thermal challenges, both cold and warm, used in dynamic infrared thermography for reconstructive surgery, and explore whether it affects the success of preoperative perforator mapping. Literature was collected from Ovid Medline, Embase, PubMed, and Cochrane. The references of the full-text articles located from the original search were also appraised. Thirteen articles were extracted for the final qualitative analysis. A systematic review was then conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Thirteen articles looked at a cold challenge, which included airflow cooling, direct contact cooling, and evaporation-based cooling. Two articles investigated warm challenges. One paper used no challenge, suggesting it unnecessary with a highly sensitive camera. All cold challenges were positively supported by a high level of flap perfusion success and/or a high level of correlation with other forms of investigation. Cold challenges were overall superior to no challenge and warm challenges; however, this conclusion is limited by the small participant size, the possibility of detection bias, and poor methodology detailing. Airflow cooling-specifically, using a desktop fan to blow air for 2 minutes-was noted to likely cause the least discomfort due to a low cooling capacity yet simultaneously maintain effectiveness and allow for a uniform cooling application. Warm challenges showed less conclusive results and were restricted by lack of studies. This topic would benefit from larger scale studies that compare multiple approaches while using standardized equipment to eliminate confounding factors.

FS-SCF network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis

Interpretability of neural networks aims at the development of models that can give information to the end-user about its inner workings and/or predictions, while keeping the high levels of performance of neural networks. In the context of fault diagnosis, interpretability is necessary for bias detection, model debugging and building trust. This is particularly relevant in the field due to the frequent applications in safety–critical systems, in which an undetected failure can result in highly detrimental outcomes. In this paper, a novel interpretable neural network model is proposed, referred to as Feature Selection and Sparse Counterfactual Generation (FS-SCF) network. It is a multi-task neural network divided into two branches: one for fault diagnosis and feature selection, and one for counterfactual generation. Thus, the model can be interpreted in terms of what are the most relevant features, and the obtained individual predictions are interpreted through counterfactuals. Also, the generated counterfactuals are used to evaluate necessity and sufficiency of each feature. The obtained rankings are then compared to the ranking obtained through the feature selection technique. The proposed approach is tested in two case studies with real data from the industry. Results show that the proposed neural network is able to achieve high levels of performance for fault diagnosis, generate interpretable counterfactuals, and determine the importance of each feature according to a criterion similar to both necessity and sufficiency.