A practical guide to estimating treatment effects in patients with rheumatic diseases using real-world data.

OBJECTIVE: Randomized controlled trials are considered the gold standard in study methodology. However, due to their study design and inclusion criteria, these studies may not capture the heterogeneity of real-world patient populations. In contrast, the lack of randomization and the presence of both measured and unmeasured confounding factors could bias the estimated treatment effect when using observational data. While causal inference methods allow for the estimation of treatment effects, their mathematical complexity may hinder their application in clinical research. METHODS: We present a practical, nontechnical guide using a common statistical package (Stata) and a motivational simulated dataset that mirrors real-world observational data from patients with rheumatic diseases. We demonstrate regression analysis, regression adjustment, inverse-probability weighting, propensity score (PS) matching and two robust estimation methods. RESULTS: Although the methods applied to control for confounding factors produced similar results, the commonly used one-to-one PS matching method could yield biased results if not thoroughly assessed. CONCLUSION: The guide we propose aims to facilitate the use of readily available methods in a common statistical package. It may contribute to robust and transparent epidemiological and statistical methods, thereby enhancing effectiveness research using observational data in rheumatology.

Compulsivity-related behavioral features of problematic usage of the internet: A scoping review of paradigms, progress, and perspectives.

Background and aims: Compulsivity contributes to the development and maintenance of multiple addictive disorders. However, the relationship between compulsivity-related cognitive features and problematic usage of the internet (PUI), an umbrella term for various internet use disorders/interfering behaviors, remains largely unclear, partly due to the multidimensional nature of compulsivity. This scoping review utilized a four-domain framework of compulsivity to consider this topic and aimed to summarize available evidence on compulsivity-related neuropsychological characteristics in PUI based on this framework. Methods: A systematic literature search was conducted by applying the combination of search term to the search engines of PubMed, PsycINFO and Web of Science. A four-domain framework of compulsivity, involving cognitive flexibility, set-shifting, attentional bias, and habit learning, was used to consider its complex structure and frequently used tasks. Main findings in related PUI studies were summarized based on this framework. Our secondary aim was to compare compulsivity-related features between different PUI subtypes. Results: Thirty-four empirical studies were retained, comprising 41 task-results and 35 independent data sets. Overall, individuals with PUI showed more consistent deficits in attentional biases and were relatively intact in set-shifting. Few studies have examined cognitive flexibility and habit learning, and more evidence is thus needed to establish reliable conclusions. Moreover, most studies focused on internet gaming disorder, whereas other PUI sub-types were not sufficiently examined. Conclusion: This systematic review highlights the use of the four-domain framework for advancing understanding of mechanisms underlying compulsivity in PUI. Related therapeutic implications and future directions are discussed.

Promoting diversity, equity and inclusion awareness in clinical documentation through postgraduate year one training.

BACKGROUND AND PURPOSE: As healthcare providers increasingly focus on emerging issues of diversity, equity and inclusion (DEI) in patient care, less is known about the training in postgraduate year one (PGY1) pharmacy residency on DEI clinical documentation considerations. This pilot project explored whether training, discussion and self-reflection within a peer review activity promoted DEI self-awareness in clinical documentation through a centralized curriculum of a multisite PGY1. EDUCATIONAL ACTIVITY AND SETTING: Building upon an established peer review of clinical documentation activity, PGY1 pharmacy residents practicing in ambulatory care settings received training on DEI considerations and completed small and large group discussions, a post-activity mixed methods survey with self-reflection prompts, and a three-month follow-up survey. FINDINGS: Twenty-two residents participated in the peer review of clinical documentation activity, DEI training and discussions. Twelve residents completed the post-activity survey with reflection prompts; 6 (50%) reported similar previous DEI training prior to residency. After the DEI training and discussions, 12 (100%) agreed or strongly agreed that their awareness of DEI documentation considerations increased; 10 (83%) would document their submitted notes differently, while one resident was unsure and one would not make changes. Twelve residents completed the follow-up survey three months following the activity. Themes from the free-text responses on key learnings collected post-activity and three-month post (respectively) included: 1) new knowledge, increased self-awareness, and intended action and 2) increased self-awareness and changes in note-making convention. SUMMARY: Integrating DEI training, discussion, and self-reflection prompts into a peer review clinical documentation activity increased self-awareness and knowledge of DEI considerations and promoted intended changes in patient care documentation for pharmacy residents. Regardless of previous training, residents reported continued self-awareness and changes in documentation conventions continued three months later.

Behavioural interventions to increase adherence to palivizumab prophylaxis in children with CHD.

OBJECTIVES: Adherence to palivizumab prophylaxis programmes is crucial to protect infants with CHD against respiratory syncytial virus infections. We analysed the effectiveness of two nudge interventions in increasing adherence. METHODS: Our study included 229 infants, and their caregivers, from five centers in Turkey in the 2020-2021 respiratory syncytial virus season. We randomly allocated caregivers to a control and two intervention groups. Caregivers in all groups were informed about the prophylaxis programme and provided a schedule. Additionally, caregivers in Intervention 1 were called two days before appointments (default bias) and were asked to plan the appointment day (implementation intention), whereas caregivers in Intervention 2 received biweekly text messages informing them about the programme's benefits (availability bias) and current adherence rate (social norm). RESULTS: Caregivers in Intervention 1 had a significantly higher adherence rate than Control (97.3% versus 90.9%) (p = 0.014). Both interventions had a significant effect on participants in their first prophylaxis season (p = 0.031, p = 0.037). Families where the father was employed had a 14.2% higher adherence rate (p = 0.001). Every additional child was associated with a 2.2% decrease in adherence rate (p = 0.02). In control, ICU admission history was associated with an 18.8% lower adherence rate (p = 0.0001), but this association disappeared in intervention groups. CONCLUSION: This is the first prospective interventional study which, in the context of palivizumab prophylaxis, analyses the effectiveness of nudge interventions based on established cognitive biases by comparing randomly generated intervention and control groups. We found that default bias and implementation intention have significant effects on adherence.Clinical trial, in the name and number "Adherence of palivizumab prophylaxis, NCT05778240" registered retrospectively. https://clinicaltrials.gov/ct2/show/NCT05778240.

Evaluating Linkage Quality of Population-Based Administrative Data for Health Service Research.

BACKGROUND: To overcome the limitations of relying on data from a single institution, many researchers have studied data linkage methodologies. Data linkage includes errors owing to legal issues surrounding personal information and technical issues related to data processing. Linkage errors affect selection bias, and external and internal validity. Therefore, quality verification for each connection method with adherence to personal information protection is an important issue. This study evaluated the linkage quality of linked data and analyzed the potential bias resulting from linkage errors. METHODS: This study analyzed claims data submitted to the Health Insurance Review and Assessment Service (HIRA DATA). The linkage errors of the two deterministic linkage methods were evaluated based on the use of the match key. The first deterministic linkage uses a unique identification number, and the second deterministic linkage uses the name, gender, and date of birth as a set of partial identifiers. The linkage error included in this deterministic linkage method was compared with the absolute standardized difference (ASD) of Cohen's according to the baseline characteristics, and the linkage quality was evaluated through the following indicators: linked rate, false match rate, missed match rate, positive predictive value, sensitivity, specificity, and F1-score. RESULTS: For the deterministic linkage method that used the name, gender, and date of birth as a set of partial identifiers, the true match rate was 83.5 and the missed match rate was 16.5. Although there was bias in some characteristics of the data, most of the ASD values were less than 0.1, with no case greater than 0.5. Therefore, it is difficult to determine whether linked data constructed with deterministic linkages have substantial differences. CONCLUSION: This study confirms the possibility of building health and medical data at the national level as the first data linkage quality verification study using big data from the HIRA. Analyzing the quality of linkages is crucial for comprehending linkage errors and generating reliable analytical outcomes. Linkers should increase the reliability of linked data by providing linkage error-related information to researchers. The results of this study will serve as reference data to increase the reliability of multicenter data linkage studies.

Comparative analysis of codon usage patterns in the chloroplast genomes of nine forage legumes.

Leguminosae is one of the three largest families of angiosperms after Compositae and Orchidaceae. It is widely distributed and grows in a variety of environments, including plains, mountains, deserts, forests, grasslands, and even waters where almost all legumes can be found. It is one of the most important sources of starch, protein and oil in the food of mankind and also an important source of high-quality forage material for animals, which has important economic significance. In our study, the codon usage patterns and variation sources of the chloroplast genome of nine important forage legumes were systematically analyzed. Meanwhile, we also constructed a phylogenetic tree based on the whole chloroplast genomes and protein coding sequences of these nine forage legumes. Our results showed that the chloroplast genomes of nine forage legumes end with A/T bases, and seven identical high-frequency (HF) codons were detected among the nine forage legumes. ENC-GC3s mapping, PR2 analysis, and neutral analysis showed that the codon bias of nine forage legumes was influenced by many factors, among which natural selection was the main influencing factor. The codon usage frequency showed that the Nicotiana tabacum and Saccharomyces cerevisiae can be considered as receptors for the exogenous expression of chloroplast genes of these nine forage legumes. The phylogenetic relationships of the chloroplast genomes and protein coding genes were highly similar, and the nine forage legumes were divided into three major clades. Among the clades Melilotus officinalis was more closely related to Medicago sativa, and Galega officinalis was more closely related to Galega orientalis. This study provides a scientific basis for the molecular markers research, species identification and phylogenetic studies of forage legumes. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-024-01421-0.

Measuring, visualizing, and diagnosing reference bias with biastools.

Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios: when the donor's variants are known and reads are simulated; when donor variants are known and reads are real; and when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias.

People see more of their biases in algorithms.

Algorithmic bias occurs when algorithms incorporate biases in the human decisions on which they are trained. We find that people see more of their biases (e.g., age, gender, race) in the decisions of algorithms than in their own decisions. Research participants saw more bias in the decisions of algorithms trained on their decisions than in their own decisions, even when those decisions were the same and participants were incentivized to reveal their true beliefs. By contrast, participants saw as much bias in the decisions of algorithms trained on their decisions as in the decisions of other participants and algorithms trained on the decisions of other participants. Cognitive psychological processes and motivated reasoning help explain why people see more of their biases in algorithms. Research participants most susceptible to bias blind spot were most likely to see more bias in algorithms than self. Participants were also more likely to perceive algorithms than themselves to have been influenced by irrelevant biasing attributes (e.g., race) but not by relevant attributes (e.g., user reviews). Because participants saw more of their biases in algorithms than themselves, they were more likely to make debiasing corrections to decisions attributed to an algorithm than to themselves. Our findings show that bias is more readily perceived in algorithms than in self and suggest how to use algorithms to reveal and correct biased human decisions.

Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling.

Primate visual cortex exhibits key organizational principles: cortical magnification, eccentricity-dependent receptive field size and spatial frequency tuning as well as radial bias. We provide compelling evidence that these principles arise from the interplay of the non-uniform distribution of retinal ganglion cells, and a quasi-uniform convergence rate from the retina to the cortex. We show that convolutional neural networks outfitted with a retinal sampling layer, which resamples images according to retinal ganglion cell density, develop these organizational principles. Surprisingly, our results indicate that radial bias is spatial-frequency dependent and only manifests for high spatial frequencies. For low spatial frequencies, the bias shifts towards orthogonal orientations. These findings introduce a novel hypothesis about the origin of radial bias. Quasi-uniform convergence limits the range of spatial frequencies (in retinal space) that can be resolved, while retinal sampling determines the spatial frequency content throughout the retina.

Unifying network model links recency and central tendency biases in working memory.

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain's ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience - producing short-term sensory history biases - naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution's mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

During cognitive tasks, our brain needs to temporarily hold and manipulate the information it is processing to decide how best to respond. This ability, known as working memory, is influenced by how the brain represents and processes the sensory world around us, which can lead to biases, such as 'central tendency'. Consider an experiment where you are presented with a metal bar and asked to recall how long it was after a few seconds. Typically, our memories, averaged over many trials of repeating this memory recall test, appear to skew towards an average length, leading to the tendency to mis-remember the bar as being shorter or longer than it actually was. This central tendency occurs in most species, and is thought to be the result of the brain learning which sensory input is the most likely to occur out of the range of possibilities. Working memory is also influenced by short-term history or recency bias, where a recent past experience influences a current memory. Studies have shown that 'turning off' a region of the rat brain called the posterior parietal cortex removes the effects of both recency bias and central tendency on working memory. Here, Boboeva et al. reveal that these two biases, which were thought to be controlled by separate mechanisms, may in fact be related. Building on the inactivation study, the team modelled a circuit of neurons that can give rise to the results observed in the rat experiments, as well as behavioural results in humans and primates. The computational model contained two modules: one of which represented a putative working memory, and another which represented the posterior parietal cortex which relays sensory information about past experiences. Boboeva et al. found that sensory inputs relayed from the posterior parietal cortex module led to recency biases in working memory. As a result, central tendency naturally emerged without needing to add assumptions to the model about which sensory input is the most likely to occur. The computational model was also able to replicate all known previous experimental findings, and made some predictions that were tested and confirmed by psychophysics tests on human participants. The findings of Boboeva et al. provide a new potential mechanism for how central tendency emerges in working memory. The model suggests that to achieve central tendency prior knowledge of how a sensory stimulus is distributed in an environment is not required, as it naturally emerges due to a volatile working memory which is susceptible to errors. This is the first mechanistic model to unify these two sources of bias in working memory. In the future, this could help advance our understanding of certain psychiatric conditions in which working memory and sensory learning are impaired.

A comparative study of J-A and Preisach improved models for core loss calculation under DC bias.

Power transformer is widely used in the power system with the rapid development of the power converters connected to the grid. When a transformer operates under DC bias conditions, its iron core loss increases significantly, causing local overheating and threatening the proper operation of the transformer. However, there are persistent difficulties in accurately assessing the core loss when the induction waveform is influenced by a DC bias. This paper first proposes improvements to the J-A and Preisach models to evaluate the core loss of the iron core under DC bias. Additionally, we incorporate the hysteresis models into the finite element method (FEM) by modifying the fundamental constitutive equations in the FEM model in order to perform a precise core loss/distribution calculation. To verify the accuracy of prediction, a transformer prototype with a laminated core is developed. The improved J-A-FEM and Preisach-FEM models were directly compared in terms of calculation accuracy, numerical implementation, and computational burden.

Triangulation of epidemiological evidence and risk of bias evaluation: A proposed framework and applied example using formaldehyde exposure and risk of myeloid leukemias.

Evidence triangulation may help identify the impact of study design elements on study findings and to tease out biased results when evaluating potential causal relationships; however, methods for triangulating epidemiologic evidence are evolving and have not been standardized. Building upon key principles of epidemiologic evidence triangulation and risk of bias assessment, and responding to the National Academies of Sciences, Engineering, and Medicine (NASEM) call for applied triangulation examples, the objective of this manuscript is to propose a triangulation framework and to apply it as an illustrative example to epidemiologic studies examining the possible relationship between occupational formaldehyde exposure and risk of myeloid leukemias (ML) including acute (AML) and chronic (CML) types. A nine-component triangulation framework for epidemiological evidence was developed incorporating study quality and ROB guidance from various federal health agencies (i.e., US EPA TSCA and NTP OHAT). Several components of the triangulation framework also drew from widely used epidemiological analytic tools such as stratified meta-analysis and sensitivity analysis. Regarding the applied example, fourteen studies were identified and assessed using the following primary study quality domains to explore potential key sources of bias: 1) study design and analysis; 2) study participation; 3) exposure assessment; 4) outcome assessment; and 5) potential confounding. Across studies, methodological limitations possibly contributing to biased results were observed within most domains. Interestingly, results from one study - often providing the largest and least-precise relative risk estimates, likely reflecting study biases, deviated from most primary study findings indicating no such associations. Triangulation of epidemiological evidence appears to be helpful in exploring inconsistent results for the identification of study results possibly reflecting various biases. Nonetheless, triangulation methodologies require additional development and application to real-world examples to enhance objectivity and reproducibility.

The Pulse of AI: Implementation of Artificial Intelligence in Healthcare and its Potential Hazards.

In this editorial, we explore the existing utilization of artificial intelligence (AI) within the healthcare industry, examining both its scope and potential harms if implemented and relied upon on a broader scale. Collaboration among corporations, government bodies, policymakers, and medical experts is essential to address potential concerns, ensuring smooth AI integration into healthcare systems.

Does Terminology Matter When Measuring Stigmatizing Attitudes About Weight? Validation of a Modified Attitudes Toward Obese Persons Scale.

Commonly used medical terms like "obesity" and "overweight" have been identified as stigmatizing. Thus, this study sought to revise a commonly used measure of weight stigmatizing attitudes, the Attitudes Toward Obese Persons (ATOP) scale. We compared the original terminology in the ATOP (e.g., "obese")to a Modified version using neutral terms (e.g., "higher weight"). We randomized participants (N = 599) to either receive the original or Modified ATOP and compared their scores. There was no significant difference between the scores of participants who received the original ATOP and the Modified ATOP, t(597) = -2.46, p = .550. Through principal component analysis, we found the Modified ATOP is best used as a 13-item unidimensional measure. Findings suggest a Modified version of the ATOP with neutral language is suitable for assessing negative attitudes about higher-weight people without sacrificing psychometric properties. Further examination of the terminology used in weight stigma measures is needed to determine how to best assess weight stigma without reinforcing stigmatizing attitudes. The findings of the present study suggest that the use of neutral terms in measures of anti-fat bias is a promising solution that warrants further investigation.

Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors.

Epidemiologic screening programs often make use of tests with small, but non-zero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation. While ignoring the finite nature of the population can yield markedly conservative estimates, direct application of a standard finite population correction (FPC) conversely leads to underestimation of variance. We uncover a way to leverage the typical FPC indirectly toward valid statistical inference. In particular, we derive a readily estimable extra variance component induced by misclassification in this specific but arguably common diagnostic testing scenario. Our approach yields a standard error estimate that properly captures the sampling variability of the usual bias-corrected maximum likelihood estimator of disease prevalence. Finally, we develop an adapted Bayesian credible interval for the true prevalence that offers improved frequentist properties (i.e., coverage and width) relative to a Wald-type confidence interval. We report the simulation results to demonstrate the enhanced performance of the proposed inferential methods.

An evaluation of synthetic data augmentation for mitigating covariate bias in health data.

Data bias is a major concern in biomedical research, especially when evaluating large-scale observational datasets. It leads to imprecise predictions and inconsistent estimates in standard regression models. We compare the performance of commonly used bias-mitigating approaches (resampling, algorithmic, and post hoc approaches) against a synthetic data-augmentation method that utilizes sequential boosted decision trees to synthesize under-represented groups. The approach is called synthetic minority augmentation (SMA). Through simulations and analysis of real health datasets on a logistic regression workload, the approaches are evaluated across various bias scenarios (types and severity levels). Performance was assessed based on area under the curve, calibration (Brier score), precision of parameter estimates, confidence interval overlap, and fairness. Overall, SMA produces the closest results to the ground truth in low to medium bias (50% or less missing proportion). In high bias (80% or more missing proportion), the advantage of SMA is not obvious, with no specific method consistently outperforming others.

Blind measurements did not confirm effects of forest fragmentation on fluctuating asymmetry of a tropical butterfly Morpho helenor.

Re-evaluation of photographs of the tropical butterfly Morpho helenor from a previous study (Pignataro et al. 2023) revealed that its conclusion regarding increased wing fluctuating asymmetry in forest edge habitats compared to forest interior habitats could not be replicated. This discrepancy likely arises from (i) original measurements not being conducted blindly, (ii) insufficient photograph quality hindering accurate landmark selection, and (iii) a lack of detailed description of the measurement protocol. The likelihood of false positive discoveries within the published data concerning the impacts of environmental stress on the fluctuating asymmetry of plants and animals is probably higher than previously assumed.

Toward a Psychology of Ideas Rather Than Demographics: Commentary on Hommel (2024).

The public will rightly not value a science that is more concerned with demographic population matching than with ideas. Taking further steps in the direction of identity politics will reduce public confidence in psychology's conclusions and reduce trust and respect. If psychology embraces demographic quotas, there will be self-selection out of the discipline, and that self-selection will harm our science.

Hematopoietic Stem Cells as an Integrative Hub Linking Lifestyle to Cardiovascular Health.

Despite breakthroughs in modern medical care, the incidence of cardiovascular disease (CVD) is even more prevalent globally. Increasing epidemiologic evidence indicates that emerging cardiovascular risk factors arising from the modern lifestyle, including psychosocial stress, sleep problems, unhealthy diet patterns, physical inactivity/sedentary behavior, alcohol consumption, and tobacco smoking, contribute significantly to this worldwide epidemic, while its underpinning mechanisms are enigmatic. Hematological and immune systems were recently demonstrated to play integrative roles in linking lifestyle to cardiovascular health. In particular, alterations in hematopoietic stem cell (HSC) homeostasis, which is usually characterized by proliferation, expansion, mobilization, megakaryocyte/myeloid-biased differentiation, and/or the pro-inflammatory priming of HSCs, have been shown to be involved in the persistent overproduction of pro-inflammatory myeloid leukocytes and platelets, the cellular protagonists of cardiovascular inflammation and thrombosis, respectively. Furthermore, certain lifestyle factors, such as a healthy diet pattern and physical exercise, have been documented to exert cardiovascular protective effects through promoting quiescence, bone marrow retention, balanced differentiation, and/or the anti-inflammatory priming of HSCs. Here, we review the current understanding of and progression in research on the mechanistic interrelationships among lifestyle, HSC homeostasis, and cardiovascular health. Given that adhering to a healthy lifestyle has become a mainstream primary preventative approach to lowering the cardiovascular burden, unmasking the causal links between lifestyle and cardiovascular health from the perspective of hematopoiesis would open new opportunities to prevent and treat CVD in the present age.

Impact of Physician Characteristics on Late-Onset Sepsis (LOS) Evaluation in the NICU.

The threshold for a late-onset sepsis (LOS) evaluation varies considerably across NICUs. This unexplained variability is probably related in part to physician bias regarding when sepsis should be "ruled out". The aim of this study is to determine if physician characteristics (race, gender, immigration status, years of experience and academic rank) effect LOS evaluation in the NICU. This study includes a retrospective chart review of all Level III NICU infants who had a LOS evaluation over 54 months. Physician characteristics were compared between positive and negative blood culture groups and whether CBC and CRP were obtained at LOS evaluations. There were 341 LOS evaluations performed during the study period. Two patients were excluded due to a contaminant. Patients in this study had a birth weight of [median (Q1, Q3)]+ 992 (720, 1820) grams and birth gestation of [median (Q1, Q3)] 276/7 (252/7, 330/7) weeks. There are 10 neonatologists in the group, 5/10 being female and 6/10 being immigrant physicians. Experienced physicians were more likely to obtain a CBC at the time of LOS evaluation. Physician characteristics of race, gender and immigration status impacted whether to include a CRP as part of a LOS evaluation but otherwise did not influence LOS evaluation, including the likelihood of bacteremia.