Magnitude Of Bias Research Articles

Bias amplification in the g-computation algorithm for time-varying treatments: a case study of industry payments and prescription of opioid products

BackgroundIt is often challenging to determine which variables need to be included in the g-computation algorithm under the time-varying setting. Conditioning on instrumental variables (IVs) is known to introduce greater bias when there is unmeasured confounding in the point-treatment settings, and this is also true for near-IVs which are weakly associated with the outcome not through the treatment. However, it is unknown whether adjusting for (near-)IVs amplifies bias in the g-computation algorithm estimators for time-varying treatments compared to the estimators ignoring such variables. We thus aimed to compare the magnitude of bias by adjusting for (near-)IVs across their different relationships with treatments in the time-varying settings.MethodsAfter showing a case study of the association between the receipt of industry payments and physicians’ opioid prescribing rate in the US, we demonstrated Monte Carlo simulation to investigate the extent to which the bias due to unmeasured confounders is amplified by adjusting for (near-)IV across several g-computation algorithms.ResultsIn our simulation study, adjusting for a perfect IV of time-varying treatments in the g-computation algorithm increased bias due to unmeasured confounding, particularly when the IV had a strong relationship with the treatment. We also found the increase in bias even adjusting for near-IV when such variable had a very weak association with unmeasured confounders between the treatment and the outcome compared to its association with the time-varying treatments. Instead, this bias amplifying feature was not observed (i.e., bias due to unmeasured confounders decreased) by adjusting for near-IV when it had a stronger association with the unmeasured confounders (≥0.1 correlation coefficient in our multivariate normal setting).ConclusionIt would be recommended to avoid adjusting for perfect IV in the g-computation algorithm to obtain a less biased estimate of the time-varying treatment effect. On the other hand, it may be recommended to include near-IV in the algorithm unless their association with unmeasured confounders is very weak. These findings would help researchers to consider the magnitude of bias when adjusting for (near-)IVs and select variables in the g-computation algorithm for the time-varying setting when they are aware of the presence of unmeasured confounding.

Informative presence bias in analyses of electronic health records-derived data: a cautionary note.

Electronic health record (EHR)-derived data are extensively used in health research. However, the pattern of patient interaction with the healthcare system can result in informative presence bias if those who have poorer health have more data recorded than healthier patients. We aimed to determine how informative presence affects bias across multiple scenarios informed by real-world healthcare utilization patterns. We conducted an analysis of EHR data from a pediatric healthcare system as well as simulation studies to characterize conditions under which informative presence bias is likely to occur. This analysis extends prior work by examining a variety of scenarios for the relationship between a biomarker and a health event of interest and the healthcare visit process. Using biomarker values gathered at both informative and noninformative visits when estimating the effect of the biomarker on the event of interest resulted in minimal bias when the biomarker was relatively stable over time but produced substantial bias when the biomarker was more volatile. Adjusting analyses for the number of prior visits within a fixed look-back window was able to reduce but not eliminate this bias. These results suggest that bias may arise frequently in commonly encountered scenarios and may not be eliminated by adjusting for prior visit intensity. Depending on the context, the estimated effect from analyses using data from all visits available may diverge from the true effect. Sensitivity analyses using only visits likely to be informative or noninformative based on visit type may aid in the assessment of the magnitude of potential bias.

Using Folded Seats‐Votes Curves to Compare Partisan Bias in the 2020 Presidential Election with Partisan Bias in the Five Other Presidential Elections in the 21st Century

Using a folded seats‐votes curve, we examine partisan bias in the 2020 presidential election and compare it to partisan bias in the five other presidential elections in the twenty‐first century. 2020 and 2016 are extreme outliers with respect to the absolute magnitude of partisan bias in the Electoral College. In 2016, 2020, and 2000 bias runs in a pro‐Republican direction; in the other three elections in this century, the opposite is true. But partisan bias can vary with where on the seats‐vote curve we look to find bias, and we identify the lowest vote share at which there is no partisan bias (if such exists).

Does the delivery mode affect post-birth neonatal serum C-reactive protein levels? A causal effect analysis.

AimTo determine if the delivery mode has a causal effect on neonatal serum C‐reactive protein (CRP) levels. If such a causal effect exists, we aim to quantify its magnitude.MethodsWe investigated the causal effect of the delivery mode on serum CRP levels 6–8 h after delivery, with appropriate statistical tools for retrospective studies, combining classical and machine‐learning methods. The statistical inference is followed by sensitivity analysis to quantify the magnitude of unobserved bias required in order to alter the study's conclusion.ResultsThis retrospective study reviewed laboratory records of neonates after birth who underwent blood tests due to suspected sepsis. A total of 440 newborns were included, 324 of which underwent a vaginal delivery, 59 an urgent caesarean delivery, and 57 an elective caesarean delivery. Our results revealed that serum CRP values following elective caesarean deliveries were 50% less than those following a vaginal delivery (P = 0.030; −0.907; 95% CI [−1.545, −0.268] in log‐CRP units). No significant effect was found for urgent caesarean deliveries compared to vaginal deliveries (P = 0.887). Those results were strengthened by (1) a sensitivity magnitude of 1.6 to unobserved bias and (2) non‐significant effects when analysis is repeated on blood collected 12–24 h after birth.ConclusionCRP concentrations in neonatal blood during the first 6–8 h of life are higher following vaginal deliveries compared to elective caesarean deliveries. Further studies with the intent of improving EONS detection should include information on the delivery mode.

The impact of moderator by confounder interactions in the assessment of treatment effect modification: a simulation study

BackgroundWhen performed in an observational setting, treatment effect modification analyses should account for all confounding, where possible. Often, such studies only consider confounding between the exposure and outcome. However, there is scope for misspecification of the confounding adjustment when estimating moderation as the effects of the confounders may themselves be influenced by the moderator. The aim of this study was to investigate bias in estimates of treatment effect modification resulting from failure to account for an interaction between a binary moderator and a confounder on either treatment receipt or the outcome, and to assess the performance of different approaches to account for such interactions.MethodsThe theory behind the reason for bias and factors that impact the magnitude of bias is explained. Monte Carlo simulations were used to assess the performance of different propensity scores adjustment methods and regression adjustment where the adjustment 1) did not account for any moderator-confounder interactions, 2) included moderator-confounder interactions, and 3) was estimated separately in each moderator subgroup. A real-world observational dataset was used to demonstrate this issue.ResultsRegression adjustment and propensity score covariate adjustment were sensitive to the presence of moderator-confounder interactions on outcome, whilst propensity score weighting and matching were more sensitive to the presence of moderator-confounder interactions on treatment receipt. Including the relevant moderator-confounder interactions in the propensity score (for methods using this) or the outcome model (for regression adjustment) rectified this for all methods except propensity score covariate adjustment. For the latter, subgroup-specific propensity scores were required. Analysis of the real-world dataset showed that accounting for a moderator-confounder interaction can change the estimate of effect modification.ConclusionsWhen estimating treatment effect modification whilst adjusting for confounders, moderator-confounder interactions on outcome or treatment receipt should be accounted for.

Comparison of the Performance of the Observation-Based Hybrid EDMF and EDMF-TKE PBL Schemes in 2020 Tropical Cyclone Forecasts from the Global-Nested Hurricane Analysis and Forecast System

Abstract Better representation of the planetary boundary layer (PBL) in numerical models is one of the keys to improving forecasts of TC structure and intensity, including rapid intensification. To meet this goal, our recent work has used observations to improve the eddy-diffusivity mass flux with prognostic turbulent kinetic energy (EDMF-TKE) PBL scheme in the Hurricane Analysis and Forecast System (HAFS). This study builds on that work by comparing a modified version of EDMF-TKE (MEDMF-TKE) with the hybrid EDMF scheme based on a K-profile method (HEDMF-KP) in the 2020 HAFS-globalnest model. Verification statistics based on 101 cases in the 2020 season demonstrate that MEDMF-TKE improves track forecasts, with a reduction in a large right bias seen in HEDMF-KP forecasts. The comparison of intensity performance is mixed, but the magnitude of low bias at early forecast hours is reduced with the use of the MEDMF-TKE scheme, which produces a wider range of TC intensities. Wind radii forecasts, particularly the radius of maximum wind speed (RMW), are also improved with the MEDMF-TKE scheme. Composites of TC inner-core structure in and above the PBL highlight and explain differences between the two sets of forecasts, with MEDMF-TKE having a stronger and shallower inflow layer, stronger eyewall vertical velocity, and more moisture in the eyewall region. A case study of Hurricane Laura shows that MEDMF-TKE better represented the subtropical ridge and thus the motion of the TC. Finally, analysis of Hurricane Delta through a tangential wind budget highlights how and why MEDMF-TKE leads to faster spinup of the vortex and a better prediction of rapid intensification.

Atomically Precise Delineation of As Antisite Defect States from Undoped Gallium Arsenide Host Lattice by Scanning Tunneling Microscopy and Spectroscopy Measurements and Density Functional Theory Calculations

Using a combination of scanning tunneling microscopy (STM) and spectroscopy with density functional theory calculations, the electronic properties of the subsurface arsenic antisite defect (AsGa) are unambiguously delineated from those of the surrounding As atoms in undoped gallium arsenide (GaAs) lattice with atomic precision. In the GaAs(110) surface with AsGalocated at the second layer (2‐AsGa), it is found that the midgap state induced by 2‐AsGamanifests as a bright contrast at the AsGaAs bond site. Furthermore, it is shown that STM images taken at large magnitudes of negative sample bias are dominated by the local density of states of neighboring surface As atoms. These states lead to a four‐lobe symmetric contrast in the filled‐state STM image around the 2‐AsGadefect. These results provide insights for surface/subsurface defect engineering at the atomic scale.

Dimension of visual information interacts with working memory in monkeys and humans

Humans demonstrate behavioural advantages (biases) towards particular dimensions (colour or shape of visual objects), but such biases are significantly altered in neuropsychological disorders. Recent studies have shown that lesions in the prefrontal cortex do not abolish dimensional biases, and therefore suggest that such biases might not depend on top-down prefrontal-mediated attention and instead emerge as bottom-up processing advantages. We hypothesised that if dimensional biases merely emerge from an enhancement of object features, the presence of visual objects would be necessary for the manifestation of dimensional biases. In a specifically-designed working memory task, in which macaque monkeys and humans performed matching based on the object memory rather than the actual object, we found significant dimensional biases in both species, which appeared as a shorter response time and higher accuracy in the preferred dimension (colour and shape dimension in humans and monkeys, respectively). Moreover, the mnemonic demands of the task influenced the magnitude of dimensional bias. Our findings in two primate species indicate that the dichotomy of top-down and bottom-up processing does not fully explain the emergence of dimensional biases. Instead, dimensional biases may emerge when processed information regarding visual object features interact with mnemonic and executive functions to guide goal-directed behaviour.

The Bias Analysis of Oil and Gas Companies’ Credit Ratings Based on Textual Risk Disclosures

Credit rating bias would affect the capital funding of oil and gas companies, and thus influence the development of the whole economy. Credit rating bias has been mostly analyzed based on different quantitative data sources, and inconsistent results have been obtained. This study first analyzes credit rating bias from the perspective of qualitative textual risk disclosures. By comparing the external credit rating with the internal risk perception expressed in the textual risk disclosures of Form 10-K filings, we can study the consistency of risk assessment of the company by the company’s management and the third-party rating agency. To be specific, four internal textual risk measures and one external risk measure are constructed to quantify the internal risk perception and external risk assessment on oil and gas companies. Then, Spearman’s rho is applied to measure the direction and magnitude of credit rating bias. In the experiment, based on the 357 samples of 174 U.S. oil and gas companies, ranging from 2009 to 2018, we find that the credit ratings mostly overestimate the internal risks perceived by the company managers, and the bias is becoming larger with the credit ratings downgraded from AAA to D.

CLIMATE CHANGE PERSPECTIVES OF THE CYCLONES AND OCEANIC HAZARDS IN THE WESTERN SOUTH ATLANTIC OCEAN

Cyclone-related oceanic hazards, such as extreme waves and surges, are frequently reported on the western South Atlantic. These events are associated with coastal erosion, coastal infrastructure damage, maritime navigation, and offshore industry incidents, being important for safety and maintenance management in ocean engineering. Present climate trends and future projections of this event are frequently linked with the expected general poleward shift of the storm track over the globe, but regional approaches revealed a slight increase in the cyclonic activity in South America 35ºS and 40ºS, which would be restricted to the coast. However, the signals of these changes are weak and frequently of the same magnitude of model biases, producing results with a lack of confidence, especially in the coastal zone. Extreme events related to waves and surges used to present large uncertainty and heterogeneity around the globe. Most of the problems regarding future estimation rely on methodological limitations that will not overrun without collaborative efforts to the improvement of observational-based science. Taking advantage of the UN Ocean Decade goals, national and regional initiatives need to collaborate towards a robust and continuous Brazilian observational network in order to face the climate crises in the country.
 
 Keywords: ocean wind-waves, coastal flooding, storm surge, extratropical cyclones, ocean waves, natural hazards.

Snow microphysical retrieval from the NASA D3R radar during ICE-POP 2018

Abstract. A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA dual-polarization, dual-frequency Doppler radar (D3R) obtained during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave single-scattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels shows that the dual-wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements. The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Ku-only, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (r=0.95) and snow water equivalent rate (r=0.92) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (−31 % and −8 %, respectively). The methods that incorporated DWR also had the best correlation to particle size (r=0.74 and r=0.71 for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (r=0.43 for All-obs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and mean absolute error (MAE) relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments.

Does one subgenome become dominant in the formation and evolution of a polyploid?

Polyploids are common in flowering plants and they tend to have more expanded ranges of distributions than their diploid progenitors. Possible mechanisms underlying polyploid success have been intensively investigated. Previous studies showed that polyploidy generates novel changes and that subgenomes in allopolyploid species often differ in gene number, gene expression levels and levels of epigenetic alteration. It is widely believed that such differences are the results of conflicts among the subgenomes. These differences have been treated by some as subgenome dominance, and it is claimed that the magnitude of subgenome dominance increases in polyploid evolution. In addition to changes which occurred during evolution, differences between subgenomes of a polyploid species may also be affected by differences between the diploid donors and changes which occurred during polyploidization. The variable genome components in many plant species are extensive, which would result in exaggerated differences between a subgenome and its progenitor when a single genotype or a small number of genotypes are used to represent a polyploid or its donors. When artificially resynthesized polyploids are used as surrogates for newly formed genotypes which have not been exposed to evolutionary selection, differences between diploid genotypes available today and those involved in the formation of the natural polyploid genotypes must also be considered. Contrary to the now widely held views that subgenome biases in polyploids are the results of conflicts among the subgenomes and that one of the parental subgenomes generally retains more genes which are more highly expressed, available results show that subgenome biases mainly reflect legacy from the progenitors and that they can be detected before the completion of polyploidization events. Further, there is no convincing evidence that the magnitudes of subgenome biases have significantly changed during evolution for any of the allopolyploid species assessed.

Atmosphere-driven cold SST biases over the western North Pacific in the GloSea5 seasonal forecast system

The predictability of the sea surface temperature (SST) in seasonal forecast systems is crucial for accurate seasonal predictions. In this study, we evaluated the prediction of SST in the Global Seasonal forecast system version 5 (GloSea5) hindcast, particularly focusing on the western North Pacific (WNP), where the SST can modify atmospheric convection and the East Asian weather. GloSea5 has a cold SST bias in the WNP that grows over at least 7 months. The bias originates from the surface net heat flux. At the beginning of model integration, the ocean receives excessive heat from the atmosphere because of the predominant positive bias in the downward shortwave radiation (SW), which rapidly decreased within a few days as cloud cover builds. Then, the negative bias in the latent heat (LH) flux increases over time and induces a negative bias in the surface net heat flux. Although the magnitude of the negative bias in LH flux gradually decreases, it remains the most significant contributor to the negative bias in the net heat flux bias for more than 250 days. Uncoupled ocean model experiments showed that the ocean model is unlikely to be the primary source of the SST bias.

Analytical covariance estimation for iterative CT reconstruction methods

Covariance of reconstruction images are useful to analyze the magnitude and correlation of noise in the evaluation of systems and reconstruction algorithms. The covariance estimation requires a big number of image samples that are hard to acquire in reality. A covariance propagation method from projection by a few noisy realizations is studied in this work. Based on the property of convergent points of cost funtions, the proposed method is composed of three steps, (1) construct a relationship between the covariance of projection and corresponding reconstruction from cost functions at its convergent point, (2) simplify the covariance relationship constructed in (1) by introducing an approximate gradient of penalties, and (3) obtain an analytical covariance estimation according to the simplified relationship in (2). Three approximation methods for step (2) are studied: the linear approximation of the gradient of penalties (LAM), the Taylor apprximation (TAM), and the mixture of LAM and TAM (MAM). TV and qGGMRF penalized weighted least square methods are experimented on. Results from statistical methods are used as reference. Under the condition of unstable 2nd derivative of penalties such as TV, the covariance image estimated by LAM accords to reference well but of smaller values, while the covarianc estimation by TAM is quite off. Under the conditon of relatively stable 2nd derivative of penalties such as qGGMRF, TAM performs well and LAM is again with a negative bias in magnitude. MAM gives a best performance under both conditions by combining LAM and TAM. Results also show that only one noise realization is enough to obtain reasonable covariance estimation analytically, which is important for practical usage. This work suggests the necessity and a new way to estimate the covariance for non-quadratically penalized reconstructions. Currently, the proposed method is computationally expensive for large size reconstructions.Computational efficiency is our future work to focus.

Quantifying Bias from Measurable and Unmeasurable Confounders Across Three Domains of Individual Determinants of Political Preferences

AbstractA core part of political research is to identify how political preferences are shaped. The nature of these questions is such that robust causal identification is often difficult to achieve, and we are not seldom stuck with observational methods that we know have limited causal validity. The purpose of this paper is to measure the magnitude of bias stemming from both measurable and unmeasurable confounders across three broad domains of individual determinants of political preferences: socio-economic factors, moral values, and psychological constructs. We leverage a unique combination of rich Swedish registry data for a large sample of identical twins, with a comprehensive battery of 34 political preference measures, and build a meta-analytical model comparing our most conservative observational (naive) estimates with discordant twin estimates. This allows us to infer the amount of bias from unobserved genetic and shared environmental factors that remains in the naive models for our predictors, while avoiding precision issues common in family-based designs. The results are sobering: in most cases, substantial bias remains in naive models. A rough heuristic is that about half of the effect size even in conservative observational estimates is composed of confounding.

Reliability and validity of an indoor local positioning system for measuring external load in ice hockey players

ABSTRACT This study determined the reliability and validity of a Kinexon local positioning system (LPS) for measuring external load in ice hockey players during an on-ice session. Fourteen ice hockey players (25.1 y, 78.6 kg, 176.9 cm) wore two LPS sensors to examine the inter-sensor reliability of the LPS during an on-ice session, and LPS speed and acceleration were measured during 40 m linear on-ice sprints and compared to a previously validated robotic sprint device to examine LPS accuracy. The coefficient of variation (CV), standard error of measurement (SEM), and intra-class correlation coefficient (ICC) were calculated for each LPS measure. Pearson’s correlations, simple linear regressions, and Bland–Altman plots were used to test the agreement and relationship between the two systems. Statistical significance was determined at p < 0.05. The majority of LPS measures were reliable (CV < 10% and ICC > 0.9) when comparing the two sensors worn by each player. Peak speed, speed at 5 m, and 0-5 m acceleration were all comparable to those reported by the robotic sprint device, with nearly perfect (peak speed and 0–5 m acceleration) and very large (speed at 5 m) magnitudes of correlation and mean biases <0.5 km/hr for speed measures and <0.01 m/s2 for acceleration. The present results demonstrate that the Kinexon LPS is reliable and accurate for investigating on-ice external load in ice hockey players when sensors are consistently secured on the back of the players’ shoulder pads.

A Simple Method to Establish Sufficiency and Stability in Meta-Analyses: With Application to Fine Particulate Matter Air Pollution and Preterm Birth.

Fine particulate matter air pollution (PM2.5) is a potential cause of preterm birth. Inconsistent findings from observational studies have motivated researchers to conduct more studies, but some degree of study heterogeneity is inevitable. The consequence of this feedback is a burgeoning research effort that results in marginal gains. The aim of this study was to develop and apply a method to establish the sufficiency and stability of estimates of associations as they have been published over time. Cohort studies identified in a recent systematic review and meta-analysis on the association between preterm birth and whole-pregnancy exposure to PM2.5 were selected. The estimates of the cohort studies were pooled with cumulative meta-analysis, whereby a new meta-analysis was run for each new study published over time. The relative risks (RR) and 95% confidence interval (CI) limits needed for a new study to move the cumulative RR to 1.00 were calculated. Findings indicate that the cumulative relative risks (cRR) for PM2.5 (cRR 1.07, 95% CI 1.03, 1.12) converged in 2015 (RR 1.07, 95% CI 1.01, 1.14). To change conclusions to a null association, a new study would need to observe a protective RR of 0.93 (95% CI limit 1.02) with precision equivalent to that achieved by all past 24 cohort studies combined. Preterm birth is associated with elevated PM2.5, and it is highly unlikely that any new observational study will alter this conclusion. Consequently, establishing whether an observational association exists is now less relevant an objective for future studies than characterising risk (magnitude, impact, pathways, populations and potential bias) and interventions. Sufficiency and stability can be effectively applied in meta-analyses and have the potential to reduce research waste.

Human spatial memory is biased towards high-calorie foods: a cross-cultural online experiment

BackgroundHuman memory appears to prioritise locations of high-calorie foods, likely as an adaptation for foraging within fluctuating ancestral food environments. Importantly, this “high-calorie bias” in human spatial memory seems to yield consequences for individual eating behaviour in modern food-abundant settings. However, as studies have mainly been conducted in European (Dutch) populations to date, we investigated whether the existence of the cognitive bias can be reasonably generalised across countries that vary on culturally-relevant domains, such as that of the USA and Japan. Furthermore, we investigated whether sociodemographic factors moderate the expression of the high-calorie spatial memory bias in different populations.MethodsIn a cross-cultural online experiment, we measured the food location memory of diverse participants from the USA (N = 72; 44.4% Male; 54 ± 15.99 years) and Japan (N = 74; 56.8% Male; 50.85 ± 17.32 years), using a validated computer-based spatial memory task with standardised images of high-calorie and low-calorie foods. To directly compare the magnitude of the high-calorie spatial memory bias in a broader cultural scope, we also included data from a previous online experiment that identically tested the food spatial memory of a Dutch sample (N = 405; 56.7% Male; 47.57 ± 17.48 years).ResultsIn the US sample, individuals more accurately recalled (i.e. had lower pointing errors for) locations of high-calorie foods versus that of low-calorie alternatives (Mean difference = -99.23 pixels, 95% CI = [-197.19, -1.28]) – regardless of one’s hedonic preferences, familiarity with foods, and encoding times. Likewise, individuals in the Japanese sample displayed an enhanced memory for locations of high-calorie (savoury-tasting) foods (Mean difference = -40.41 pixels, 95% CI = [-76.14, -4.68]), while controlling for the same set of potential confounders. The magnitude of the high-calorie bias in spatial memory was similar across populations (i.e. the USA, Japan, and the Netherlands), as well as across diverse sociodemographic groups within a population.ConclusionsOur results demonstrate that the high-calorie bias in spatial memory transcends sociocultural boundaries. Since the cognitive bias may negatively impact on our dietary decisions, it would be wise to invest in strategies that intervene on our seemingly universal ability to efficiently locate calorie-rich foods.

Assessment of Implicit Gender Bias During Evaluation of Procedural Competency Among Emergency Medicine Residents

Gender disparities exist throughout medicine. Recent studies have highlighted an attainment gap between male and female residents in performance evaluations on Accreditation Council for Graduate Medical Education (ACGME) milestones. Because of difficulties in blinding evaluators to gender, it remains unclear whether these observed disparities are because of implicit bias or other causes. To estimate the magnitude of implicit gender bias in assessments of procedural competency in emergency medicine residents and whether the gender of the evaluator is associated with identified implicit gender bias. A cross-sectional study was performed from 2018 to 2020 in which emergency medicine residency faculty assessed procedural competency by evaluating videos of residents performing 3 procedures in a simulated environment. They were blinded to the intent of the study. Proceduralists were filmed performing each procedure from 2 different viewpoints simultaneously by 2 different cameras. One was a gender-blinded (ie, hands-only) view, and the other a wide-angled gender-evident (ie, whole-body) view. The faculty evaluators viewed videos in a random order and assessed procedural competency on a global rating scale with extensive validity evidence for the evaluation of video-recorded procedural performance. The primary outcome was to determine if there was a difference in the evaluation of procedural competency based on gender. The secondary outcome was to determine if there was a difference in the evaluations based on the gender of the evaluator. Fifty-one faculty evaluators enrolled from 19 states, with 22 male participants (43.1%), 29 female participants (56.9%), and a mean (SD) age of 37 (6.4) years. Each evaluator assessed all 60 procedures: 30 gender-blinded (hands-only view) videos and 30 identical gender-evident (wide angle) videos. There were no statistically significant differences in the study evaluators' scores of the proceduralists based on their gender, and the gender of the evaluator was not associated with the difference in mean scores. In this study, we did not identify a difference in the evaluation of procedural competency based upon the gender of the resident proceduralist or the gender of the faculty evaluator.

Influence of sample size and number of age classes on characterization of ageing error in paired-age comparisons

Diagnosis of ageing error is critical to the proper interpretation of age data used in fisheries science and management. However, the influence of sample size and number of age classes on the characterization of ageing error has not been thoroughly evaluated. We conducted a simulation study of ageing error diagnostics for paired-age comparisons across 648 scenarios differing in 1) number of age classes, 2) total number of samples aged, 3) trend in sample size by age, and 4) magnitude and type of imprecision and bias. Imprecision was identified by comparing average coefficient of variation (ACV) with two common thresholds. Bias was evaluated using maximally (McNemar’s), diagonally (Evans & Hoenig), and unpooled tests of symmetry (Bowker’s). Imprecision was identified less frequently at low to moderate (x¯=6% of runs) levels of random vs high (x¯=55% of runs) error, and ACV was artificially inflated in the presence of bias. McNemar’s and Evans & Hoenig bias tests outperformed Bowker’s (x¯=4% vs 29% false positives), particularly at large sample sizes, and its use is strongly discouraged. This study can help guide the interpretation of ageing error studies and their products (e.g., ageing error matrices) used to inform stock assessment and management.