Metacognitive biases have been repeatedly associated with transdiagnostic psychiatric dimensions of \'anxious-depression\' and \'compulsivity and intrusive thought\', cross-sectionally. To progress our understanding of the underlying neurocognitive mechanisms, new methods are required to measure metacognition remotely, within individuals over time. We developed a gamified smartphone task designed to measure visuo-perceptual metacognitive (confidence) bias and investigated its psychometric properties across two studies (N = 3410 unpaid citizen scientists, N = 52 paid participants). We assessed convergent validity, split-half and test-retest reliability, and identified the minimum number of trials required to capture its clinical correlates. Convergent validity of metacognitive bias was moderate (r(50) = 0.64, p < 0.001) and it demonstrated excellent split-half reliability (r(50) = 0.91, p < 0.001). Anxious-depression was associated with decreased confidence (β =  - 0.23, SE = 0.02, p < 0.001), while compulsivity and intrusive thought was associated with greater confidence (β = 0.07, SE = 0.02, p < 0.001). The associations between metacognitive biases and transdiagnostic psychiatry dimensions are evident in as few as 40 trials. Metacognitive biases in decision-making are stable within and across sessions, exhibiting very high test-retest reliability for the 100-trial (ICC = 0.86, N = 110) and 40-trial (ICC = 0.86, N = 120) versions of Meta Mind. Hybrid \'self-report cognition\' tasks may be one way to bridge the recently discussed reliability gap in computational psychiatry.

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BACKGROUND: Peripherally inserted central catheters (PICCs) facilitate diagnostic and therapeutic interventions in health care. PICCs can fail due to infective and non-infective complications, which PICC materials and design may contribute to, leading to negative sequelae for patients and healthcare systems.
OBJECTIVE: To assess the effectiveness of PICC material and design in reducing catheter failure and complications.
METHODS: The University of Queensland and Cochrane Vascular Information Specialist searched the Cochrane Vascular Specialised Register, CENTRAL, MEDLINE, Embase, and CINAHL databases and the WHO ICTRP and ClinicalTrials.gov trials registers to 16 May 2023. We aimed to identify other potentially eligible trials or ancillary publications by searching the reference lists of retrieved included trials, as well as relevant systematic reviews, meta-analyses, and health technology assessment reports. We contacted experts in the field to ascertain additional relevant information.
METHODS: We included randomised controlled trials (RCTs) evaluating PICC design and materials.
METHODS: We used standard Cochrane methods. Our primary outcomes were venous thromboembolism (VTE), PICC-associated bloodstream infection (BSI), occlusion, and all-cause mortality. Secondary outcomes were catheter failure, PICC-related BSI, catheter breakage, PICC dwell time, and safety endpoints. We assessed the certainty of evidence using GRADE.
RESULTS: We included 12 RCTs involving approximately 2913 participants (one multi-arm study). All studies except one had a high risk of bias in one or more risk of bias domain. Integrated valve technology compared to no valve technology for peripherally inserted central catheter design Integrated valve technology may make little or no difference to VTE risk when compared with PICCs with no valve (risk ratio (RR) 0.71, 95% confidence interval (CI) 0.19 to 2.63; I² = 0%; 3 studies; 437 participants; low certainty evidence). We are uncertain whether integrated valve technology reduces PICC-associated BSI risk, as the certainty of the evidence is very low (RR 0.20, 95% CI 0.01 to 4.00; I² = not applicable; 2 studies (no events in 1 study); 257 participants). Integrated valve technology may make little or no difference to occlusion risk when compared with PICCs with no valve (RR 0.86, 95% CI 0.53 to 1.38; I² = 0%; 5 studies; 900 participants; low certainty evidence). We are uncertain whether use of integrated valve technology reduces all-cause mortality risk, as the certainty of evidence is very low (RR 0.85, 95% CI 0.44 to 1.64; I² = 0%; 2 studies; 473 participants). Integrated valve technology may make little or no difference to catheter failure risk when compared with PICCs with no valve (RR 0.80, 95% CI 0.62 to 1.03; I² = 0%; 4 studies; 720 participants; low certainty evidence). We are uncertain whether integrated-valve technology reduces PICC-related BSI risk (RR 0.51, 95% CI 0.19 to 1.32; I² = not applicable; 2 studies (no events in 1 study); 542 participants) or catheter breakage, as the certainty of evidence is very low (RR 1.05, 95% CI 0.22 to 5.06; I² = 20%; 4 studies; 799 participants). Anti-thrombogenic surface modification compared to no anti-thrombogenic surface modification for peripherally inserted central catheter design We are uncertain whether use of anti-thrombogenic surface modified catheters reduces risk of VTE (RR 0.67, 95% CI 0.13 to 3.54; I² = 15%; 2 studies; 257 participants) or PICC-associated BSI, as the certainty of evidence is very low (RR 0.20, 95% CI 0.01 to 4.00; I² = not applicable; 2 studies (no events in 1 study); 257 participants). We are uncertain whether use of anti-thrombogenic surface modified catheters reduces occlusion (RR 0.69, 95% CI 0.04 to 11.22; I² = 70%; 2 studies; 257 participants) or all-cause mortality risk, as the certainty of evidence is very low (RR 0.49, 95% CI 0.05 to 5.26; I² = not applicable; 1 study; 111 participants). Use of anti-thrombogenic surface modified catheters may make little or no difference to risk of catheter failure (RR 0.76, 95% CI 0.37 to 1.54; I² = 46%; 2 studies; 257 participants; low certainty evidence). No PICC-related BSIs were reported in one study (111 participants). As such, we are uncertain whether use of anti-thrombogenic surface modified catheters reduces PICC-related BSI risk (RR not estimable; I² = not applicable; very low certainty evidence). We are uncertain whether use of anti-thrombogenic surface modified catheters reduces the risk of catheter breakage, as the certainty of evidence is very low (RR 0.15, 95% CI 0.01 to 2.79; I² = not applicable; 2 studies (no events in 1 study); 257 participants). Antimicrobial impregnation compared to non-antimicrobial impregnation for peripherally inserted central catheter design We are uncertain whether use of antimicrobial-impregnated catheters reduces VTE risk (RR 0.54, 95% CI 0.05 to 5.88; I² = not applicable; 1 study; 167 participants) or PICC-associated BSI risk, as the certainty of evidence is very low (RR 2.17, 95% CI 0.20 to 23.53; I² = not applicable; 1 study; 167 participants). Antimicrobial-impregnated catheters probably make little or no difference to occlusion risk (RR 1.00, 95% CI 0.57 to 1.74; I² = 0%; 2 studies; 1025 participants; moderate certainty evidence) or all-cause mortality (RR 1.12, 95% CI 0.71 to 1.75; I² = 0%; 2 studies; 1082 participants; moderate certainty evidence). Antimicrobial-impregnated catheters may make little or no difference to risk of catheter failure (RR 1.04, 95% CI 0.82 to 1.30; I² = not applicable; 1 study; 221 participants; low certainty evidence). Antimicrobial-impregnated catheters probably make little or no difference to PICC-related BSI risk (RR 1.05, 95% CI 0.71 to 1.55; I² = not applicable; 2 studies (no events in 1 study); 1082 participants; moderate certainty evidence). Antimicrobial-impregnated catheters may make little or no difference to risk of catheter breakage (RR 0.86, 95% CI 0.19 to 3.83; I² = not applicable; 1 study; 804 participants; low certainty evidence).
CONCLUSIONS: There is limited high-quality RCT evidence available to inform clinician decision-making for PICC materials and design. Limitations of the current evidence include small sample sizes, infrequent events, and risk of bias. There may be little to no difference in the risk of VTE, PICC-associated BSI, occlusion, or mortality across PICC materials and designs. Further rigorous RCTs are needed to reduce uncertainty.

Hierarchical predictive processing provides a framework outlining how prior expectations shape perception and cognition. Here, we highlight hierarchical predictive processing as a framework for explaining how social context and group-based social knowledge can directly shape intergroup perception. More specifically, we argue that hierarchical predictive processing confers a uniquely valuable toolset to explain extant findings and generate novel hypotheses for intergroup perception. We first provide an overview of hierarchical predictive processing, specifying its primary theoretical assumptions. We then review evidence showing how prior knowledge influences intergroup perception. Next, we outline how hierarchical predictive processing can account well for findings in the intergroup perception literature. We then underscore the theoretical strengths of hierarchical predictive processing compared to other frameworks in this space. We finish by outlining future directions and laying out hypotheses that test the implications of hierarchical predictive processing for intergroup perception and intergroup cognition more broadly. Taken together, hierarchical predictive processing provides explanatory value and capacity for novel hypothesis generation for intergroup perception.

Research has shown that X-rays and fundus images can classify gender, age group, and race, raising concerns about bias and fairness in medical AI applications. However, the potential for physiological sounds to classify sociodemographic traits has not been investigated. Exploring this gap is crucial for understanding the implications and ensuring fairness in the field of medical sound analysis. We aimed to develop classifiers to determine gender (men/women) based on heart sound recordings and using machine learning (ML). Data-driven ML analysis. We utilized the open-access CirCor DigiScope Phonocardiogram Dataset obtained from cardiac screening programs in Brazil. Volunteers < 21 years of age. Each participant completed a questionnaire and underwent a clinical examination, including electronic auscultation at four cardiac points: aortic (AV), mitral (MV), pulmonary (PV), and tricuspid (TV). We used Mel-frequency cepstral coefficients (MFCCs) to develop the ML classifiers. From each patient and from each auscultation sound recording, we extracted 10 MFCCs. In sensitivity analysis, we additionally extracted 20, 30, 40, and 50 MFCCs. The most effective gender classifier was developed using PV recordings (AUC ROC = 70.3%). The second best came from MV recordings (AUC ROC = 58.8%). AV and TV recordings produced classifiers with an AUC ROC of 56.4% and 56.1%, respectively. Using more MFCCs did not substantially improve the classifiers. It is possible to classify between males and females using phonocardiogram data. As health-related audio recordings become more prominent in ML applications, research is required to explore if these recordings contain signals that could distinguish sociodemographic features.

BACKGROUND: Measles seroprevalence data have potential to be a useful tool for understanding transmission dynamics and for decision making efforts to strengthen immunization programs. In this study, we conducted a systematized review and bias assessment of all primary data on measles seroprevalence in low- and middle-income countries (as defined by World Bank 2021 income classifications) published from 1962 to 2021.
METHODS: On 9 March 2022, we searched PubMed for all available data. We included studies containing primary data on measles seroprevalence and excluded studies if they were clinical trials or brief reports, from only health-care workers, suspected measles cases, or only vaccinated persons. We extracted all available information on measles seroprevalence, study design, and seroassay protocol. We conducted a bias assessment based on multiple categories and classified each study as having low, moderate, severe, or critical bias. This review was registered with PROSPERO (CRD42022326075).
RESULTS: We identified 221 relevant studies across all World Health Organization regions, decades, and unique age ranges. The overall crude mean seroprevalence across all studies was 78.0% (SD: 19.3%), and the median seroprevalence was 84.0% (IQR: 72.8-91.7%). We classified 80 (36.2%) studies as having severe or critical overall bias. Studies from country-years with lower measles vaccine coverage or higher measles incidence had higher overall bias.
CONCLUSIONS: While many studies have substantial underlying bias, many studies still provide some insights or data that could be used to inform modelling efforts to examine measles dynamics and programmatic decisions to reduce measles susceptibility.

Information that comes from the environment reaches the brain-and-body system via sensory inputs that can operate outside of conscious awareness and influence decision processes in different ways. Specifically, decision-making processes can be influenced by various forms of implicit bias derived from individual-related factors (e.g., individual differences in decision-making style) and/or stimulus-related information, such as visual input. However, the relationship between these subjective and objective factors of decision making has not been investigated previously in professionals with varying seniority. This study explored the relationship between decision-making style and cognitive bias resistance in professionals compared with a group of newcomers in organisations. A visual \"picture-picture\" semantic priming task was proposed to the participants. The task was based on primes and probes\' category membership (animals vs. objects), and after an animal prime stimulus presentation, the probe can be either five objects (incongruent condition) or five objects and an animal (congruent condition). Behavioural (i.e., accuracy-ACC, and reaction times-RTs) and self-report data (through the General Decision-Making Scale administration) were collected. RTs represent an indirect measure of the workload and cognitive effort required by the task, as they represent the time it takes the nervous system to receive and integrate incoming sensory information, inducing the body to react. For both groups, the same level of ACC in both conditions and higher RTs in the incongruent condition were found. Interestingly, for the group of professionals, the GDMS-dependent decision-making style negatively correlates with ACC and positively correlates with RTs in the congruent condition. These findings suggest that, under the incongruent decision condition, the resistance to cognitive bias requires the same level of cognitive effort, regardless of seniority. However, with advancing seniority, in the group of professionals, it has been demonstrated that a dependent decision-making style is associated with lower resistance to cognitive bias, especially in conditions that require simpler decisions. Whether this result depends on age or work experience needs to be disentangled from future studies.

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.

OBJECTIVE: Death data from cohorts of academicians have been used to estimate pandemic excess deaths. We aimed to evaluate the validity of this approach.
METHODS: Data were analyzed from living and deceased member lists from Mainland China, UK and Greece academies; and Nobel laureates (and US subset thereof). Samples of early elected academicians were probed for unrecorded deaths; datasets overtly missing deaths were excluded from further analyses. Actuarial risks were compared against the general population in the same country in respective age strata. Relative incidence risk increases in death in active pandemic periods were compared to population-wide pandemic excess death estimates for the same country.
RESULTS: Royal Society and Academy of Athens datasets overtly missed deaths. Pre-pandemic death rates were 4-12-fold lower in the Chinese Academy of Engineering (CAE) versus respective age strata of the Mainland China population. A +158% relative increase in death risk was seen in CAE data during the first 12-months of wide viral spread. Both increases (+34% in British Academy) and decreases (-27% in US Nobel laureates) in death rates occurred in pandemic (2020-22) versus pre-pandemic (2017-2019) years; point estimates were far from known excess deaths in the respective countries (+6% and +14%, respectively). Published excess death estimates for urban-dwelling Mainland China selectively analyzed CAE that had double the pandemic death rates than another Chinese academy (Chinese Academy of Sciences).
CONCLUSIONS: Missingness, lack of representativeness, large uncertainty, and selective analysis reporting make data from academy rosters unreliable for estimating general population excess deaths.

Ideally, measuring exposures to volatile organic compounds should allow for modifying sampling duration without loss in sensitivity. Traditional sorbent-based sampling can vary sampling duration, but sensitivity may be affected when capturing shorter tasks. Diaphragm and capillary flow controllers allow for a range of flow rates and sampling durations for air sampling with evacuated canisters. The goal of this study was to evaluate the extent to which commercialized capillary flow controllers satisfy the bias (±10%) and accuracy (±25%) criteria for air sampling methods as established by the National Institute for Occupational Safety and Health (NIOSH) using the framework of ASTM D6246 Standard Practice for Evaluating the Performance of Diffusive Samplers to compare their performance with diaphragm flow controllers in a long-term field study. Phase 1 consisted of a series of laboratory tests to evaluate capillary flow controller flow rates with respect to variations in temperature (-15-24 °C). The results demonstrated a slight increase in flow rate with lower temperatures. In Phase 2, the capillary flow controller was evaluated utilizing a matrix of parameters, including time-weighted average concentration, peak concentration (50-100× base concentration), air velocity across the sampler inlet (0.41-0.5 m/s), relative humidity (20-80%), and temperature (10-32 °C). Comparison of challenge concentrations with reference concentrations revealed the aggregate bias and overall accuracy for four tested compounds to be within the range of criteria for both NIOSH and ASTM standards. Additionally, capillary flow controllers displayed lower variability in flow rate and measured concentration (RSD: 2.4% and 4.3%, respectively) when compared with diaphragm flow controllers (RSD: 6.9% and 7.2%, respectively) for 24-hr laboratory tests. Phase 3 involved further testing of flow rate variability for both diaphragm and capillary flow controllers in a field study. The capillary flow controller displayed a lower level of variability (RSD: 5.2%) than the diaphragm flow controller (RSD: 8.0%) with respect to flow rate, while allowing for longer durations of sampling.