Potential Sources Of Bias Research Articles

Asperity Size and Neighboring Segments Can Change the Frictional Response and Fault Slip Behavior: Insights From Laboratory Experiments and Numerical Simulations

AbstractAccurate assessment of the rate and state friction parameters of rocks is essential for producing realistic earthquake rupture scenarios and, in turn, for seismic hazard analysis. Those parameters can be directly measured on samples, or indirectly based on inversion of coseismic or postseismic slip evolution. However, both direct and indirect approaches require assumptions that might bias the results. Aiming to reduce the potential sources of bias, we take advantage of a downscaled analog model reproducing megathrust earthquakes. We couple the simulated annealing algorithm with quasi‐dynamic numerical models to retrieve rate and state parameters reproducing the recurrence time, rupture duration and slip of the analog model, in the ensemble. Then, we focus on how the asperity size and the neighboring segments' properties control the seismic cycle characteristics and the corresponding variability of rate and state parameters. We identify a tradeoff between (a–b) of the asperity and (a–b) of neighboring creeping segments, with multiple parameter combinations that allow mimicking the analog model behavior. Tuning of rate and state parameters is required to fit laboratory experiments with different asperity lengths. Poorly constrained frictional properties of neighboring segments are responsible for uncertainties of (a–b) of the asperity in the order of per mille. Roughly one order of magnitude larger uncertainties derive from asperity size. Those results provide a glimpse of the variability that rate and state friction estimates might have when used as a constraint to model fault slip behavior in nature.

Deconstructing demographic bias in speech-based machine learning models for digital health.

Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes. This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one's demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state. Results are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models' ability to accurately estimate healthcare outcomes for the sensitive groups. These findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications.

Confounding mitigation for the exposure-response relationship of bevacizumab in colorectal cancer patients.

The exposure-response relationship of bevacizumab may be confounded by various factors, including baseline characteristics, time-dependent target engagement and recursive relationships between exposure and response, requiring effective mitigation. This study aimed to investigate the exposure-response relationships of bevacizumab in metastatic colorectal cancer (mCRC) patients while mitigating potential biases. Bevacizumab pharmacokinetics was described using target-mediated drug disposition modelling. Relationships between target kinetics, progression-free (PFS) and overall (OS) survivals were assessed using joint pharmacokinetic and parametric hazard function models. Both prognostic-driven and response-driven potential biases were mitigated. These models evaluated the impact of increased antigen target levels, clearance and intensified dosing regimen on survival. Estimated target-mediated pharmacokinetic parameters in 130 assessed patients were baseline target levels (R0 = 8.4nM), steady-state dissociation constant (KSS = 10 nM) and antibody-target complexes elimination constant (kint = 0.52 day-1). The distribution of R0 was significantly associated with increased baseline concentrations of carcinoembryonic antigen, circulating vascular endothelial growth factor and the presence of extrahepatic metastases. Unbound target levels (R) significantly influenced both progression and death hazard functions. Increasing baseline target levels and/or clearance values led to decreased bevacizumab unbound concentrations, increased R levels and shortened PFS and OS, while increasing bevacizumab dose led to decreased R and longer survival. This study is the first to demonstrate the relationship between bevacizumab concentrations, target involvement and clinical efficacy by effectively mitigating potential sources of bias. Most of the target amount may be tumoural in mCRC. Future studies should provide a more in-depth description of this relationship.

Appraising systematic reviews: a comprehensive guide to ensuring validity and reliability.

Systematic reviews play a crucial role in evidence-based practices as they consolidate research findings to inform decision-making. However, it is essential to assess the quality of systematic reviews to prevent biased or inaccurate conclusions. This paper underscores the importance of adhering to recognized guidelines, such as the PRISMA statement and Cochrane Handbook. These recommendations advocate for systematic approaches and emphasize the documentation of critical components, including the search strategy and study selection. A thorough evaluation of methodologies, research quality, and overall evidence strength is essential during the appraisal process. Identifying potential sources of bias and review limitations, such as selective reporting or trial heterogeneity, is facilitated by tools like the Cochrane Risk of Bias and the AMSTAR 2 checklist. The assessment of included studies emphasizes formulating clear research questions and employing appropriate search strategies to construct robust reviews. Relevance and bias reduction are ensured through meticulous selection of inclusion and exclusion criteria. Accurate data synthesis, including appropriate data extraction and analysis, is necessary for drawing reliable conclusions. Meta-analysis, a statistical method for aggregating trial findings, improves the precision of treatment impact estimates. Systematic reviews should consider crucial factors such as addressing biases, disclosing conflicts of interest, and acknowledging review and methodological limitations. This paper aims to enhance the reliability of systematic reviews, ultimately improving decision-making in healthcare, public policy, and other domains. It provides academics, practitioners, and policymakers with a comprehensive understanding of the evaluation process, empowering them to make well-informed decisions based on robust data.

Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review.

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

Attention‐deficit/hyperactivity disorder in old age and the risk of dementia: A national cohort study in Israel

AbstractBackgroundEvidence of the association between Attention‐Deficit/Hyperactivity Disorder (ADHD) and the risk of dementia is scarce, inconsistent, and subject to potential sources of bias that are as yet unconsidered. Our objective was to examine the association between ADHD and dementia risk.MethodA prospective national cohort study born 1933‐1952, without a dementia diagnosis for two years, were followed up for incident dementia from 2002‐2020. Information was from electronic health records in a healthcare maintenance organization in Israel. ADHD diagnosis was classified as present from the age of the first diagnosis, otherwise absent. Competing risk Cox regression models were fit to quantify the risk of incident dementia with the Hazard Ratio (HR) and their associated 95% Confidence Intervals (CI) with inverse probability weights, age as the underlying time‐scale, unadjusted and, in the primary analysis, adjusted by 18 covariates.ResultOf 110,196 participants aged 50‐70 years in 2002, 56,914 (51.6%) were female. During follow‐up there were a total of 746 (0.7%) cases of ADHD, and 8,339 (7.6%) of incident dementia. The percentage of incident dementia was 13.1% (N = 98) among those with ADHD present and 7.5% (N = 8,241) among those with ADHD absent. In the primary analysis, the group with ADHD was statistically significantly (P<.05) associated with a higher risk of dementia (unadjusted: HR = 3.57, CI = 2.89, 4.42; adjusted HR = 2.73, CI = 2.04, 3.65) compared to the group with ADHD absent. Eight of ten sensitivity analyses replicated the statistically significant association observed in the primary analysis. Psychostimulant medication offset the association between ADHD and dementia risk to null, and evidence of reverse causation was mild.ConclusionADHD is associated with increased dementia risk. Policymakers, caregivers, patients, and clinicians may wish to monitor ADHD in old age reliably.

Joint cosmological and gravitational-wave population inference using dark sirens and galaxy catalogues

In the absence of numerous gravitational-wave detections with confirmed electromagnetic counterparts, the “dark siren” method has emerged as a leading technique of gravitational-wave cosmology. The method allows redshift information of such events to be inferred statistically from a catalogue of potential host galaxies. Due to selection effects, dark siren analyses necessarily depend on the mass distribution of compact objects and the evolution of their merger rate with redshift. Informative priors on these quantities will impact the inferred posterior constraints on the Hubble constant (H 0). It is thus crucial to vary these unknown distributions during an H 0 inference. This was not possible in earlier analyses due to the high computational cost, restricting them to either excluding galaxy catalogue information, or fixing the gravitational-wave population mass distribution and risking introducing bias to the H 0 measurement. This paper introduces a significantly enhanced version of the Python package gwcosmo, which allows joint estimation of cosmological and compact binary population parameters. This thereby ensures the analysis is now robust to a major source of potential bias. The gravitational-wave events from the Third Gravitational-Wave Transient Catalogue are reanalysed with the GLADE+ galaxy catalogue, and an updated, more reliable measurement of H 0 = 69+12 -7 km s-1 Mpc-1 is found (maximum a posteriori probability and 68% highest density interval). This improved method will enable cosmological analyses with future gravitational-wave detections to make full use of the information available (both from galaxy catalogues and the compact binary population itself), leading to promising new independent bounds on the Hubble constant.

Bias in Acoustic‐ and Linguistic‐based Classifications of Alzheimer’s Disease

AbstractBackgroundIt is crucial to identify patients with Alzheimer’s disease (AD). To do so, various attempts have been made to develop AI and non‐AI assessments, among which some focus on developing acoustic‐ and linguistic‐based classifiers. They are based on the detection of impairments in their language and speech, which can manifest years before other cognitive impairments associated with AD appear. The fact is that most of the current vocal and language classifiers of AD have been trained using the Pitt corpus, which is an imbalanced class and gender dataset. These two characteristics could be enough to bias such classifiers making them untrustworthy and unsuitable for integration into AD care settings. This paper presents a novel method for collecting vocal data to reduce the impact of potential sources of bias on acoustic and linguistic classifiers for AD.MethodWe will use a data collection approach that collects voices from participants (i.e., patients with AD and healthy controls) with diversity in race, gender, educational, socioeconomic, and cultural backgrounds. Participants will be asked to perform diverse language tasks, including word association, verbal fluency, and grammaticality judgment tasks. Following such an approach can ensure we collect oral data that wouldn’t be affected by selection, recruitment, sociolinguistics, and gender biases.ResultsThe main result of this study is the creation of a benchmark vocal dataset from diverse gender and racial groups and ensuring that the data is representative of the population with AD. We expect such data to be used for evaluating and validating and help AI developers to successfully develop fair acoustic and linguistic classifiers of AD. It, in its turn, can motivate healthcare professionals to employ these systems as assistants to identify patients with AD from their voices quickly.ConclusionThe Pitt corpus is a biased data set. Thus, acoustic and linguistic classifiers trained upon it can not be considered trustworthy AI systems. The AI developers that aim to deploy vocal systems into Alzheimer’s disease care settings would need unbiased vocal data. This study proposed a method to collect such verbal data.

Direct oral anticoagulants versus vitamin k antagonists in patients with atrial fibrillation and bioprosthesis: a systematic review and meta-analysis

Abstract Background/Introduction The large randomized clinical trials published to date have shown that direct oral anticoagulants (DOACs) are as or more effective than vitamin K antagonists (VKAs) in the prevention of thromboembolic events in atrial fibrillation (AF) and generally show a better bleeding risk profile. However, patients with bioprosthetic valves have been underrepresented in RCTs. The aim of this study was to assess the safety and efficacy of DOAC in patients with AF and bioprosthetic valves through a systematic review and meta-analysis including recent evidence from comparative cohort studies and real-world results. Methods A systematic review of the scientific literature and a subsequent meta-analysis were carried out. We included RCTs or comparative observational studies, published from January 2017 to January 2022 comparing DOACs versus vitamin K antagonists in patients with AF or atrial flutter and bioprostheses. We collected data of all-cause mortality, major bleeding, and systemic embolism. The primary outcome of interest was all-cause mortality. Secondary outcomes were systemic embolism or stroke. The primary safety outcome was major bleeding. Results Our analysis included 12 studies, 4 RCTs and 8 observational studies that enrolled a total of 30.283 patients. All-cause mortality 6 observational and 3 RCT were combined. A 9% reduction was found for all-cause mortality in patients treated with DOACs (HR,0.91; P=.0068;95%CI,0.85–0.97;I2=8%). Stroke and systemic embolism 4 RCT and 8 observational studies were combined. No evidence showing a significant effect of DOAC therapy on risk reduction for stroke and embolism was obtained (HR, 0.87; P =.29; 95%CI, 0.67–1.14; I2=45%). Major bleeding The results of the 12 studies were combined. A robustness analysis was performed, and the ENVISAGE RCT was identified as atypical. We therefore proceeded to re-run the meta-analysis without this study’s results. The overall effect size was HR, 0.79 (P = .0001; 95%CI, 0.73-0.85; I2 = 0%), with a 21.1% reduction of major bleeding with DOAC therapy compared with VKA. The present study has several limitations. It has the inherent limitations of the studies included and the combination of findings from comparative observational cohort studies and ad hoc studies from RCTs, which entails a potential source of bias. Second, individual risks for each type of bioprosthesis were not studied. Finally, the four currently marketed DOACs were included in our meta-analysis, but their efficacy and safety were not evaluated separately. Conclusions Our study found that DOAC therapy is associated with a significant reduction of all-cause mortality with no significant increase in the risk of systemic embolism or stroke. This reduction could be attributed to a decrease in major bleeding. Our results therefore suggest that DOACs could be a safer alternative than VKAs in this group of patients.

Implementation of a Chief Resident Selection Process Designed to Mitigate Bias: Lessons Learned.

Chief residency selection processes are often opaque and beset by bias, which can result in disparities in who is selected for this important role. As a chief residency can lead to future academic and/or leadership positions, efforts to increase diversity in academic medicine and physician leadership may be aided by an inclusive chief resident (CR) selection process designed to mitigate bias. Objective: To implement and evaluate the acceptability of a CR selection process that is inclusive and designed to mitigate bias. Methods: In the 2021-2022 academic year, we designed and implemented a CR selection process aligned with published strategies known to mitigate bias in academic recruitment. The four-step opt-out CR selection process included a nomination survey, structured interviews, a clinical review, and a holistic review of each candidate. Each step was clearly delineated, assigned a specific number of points, and scored on a designated rubric. The candidates with the highest and second-highest number of points were awarded the two CR positions. Our selection process excluded examination scoresand precluded consideration of "fit" between the selected CRs, as these are known sources of potential bias. In January 2023, we surveyed our department to obtain post-implementation feedback and to assess satisfaction with the process, before repeating the process for 2022-2023. Survey response rates were47% (14/30) for residents and 29% (18/63) for departmental faculty. The majority of responding residents (64%) and faculty (100%) were satisfied with the CR selection process, finding it fair and inclusive. Nearly 80% of residents and 100% of faculty wished to repeat the process in 2022-2023. Conclusions: An inclusive CR selection process utilizing strategies to mitigate bias was feasible, and acceptable to residents and faculty. We recommend that residency training programs make efforts to implement CR selection practices that are inclusive and aim to mitigate bias.

Tuberculosis infection control practice among healthcare workers in Ethiopia: a protocol for systematic review and meta-analysis

BackgroundTuberculosis (TB) is a main concern of health care workers in sub-Saharan Africa. Healthcare workers have the potential to have contact with TB patients and are the main stakeholders in...

Practical application of the linkage disequilibrium method for estimating contemporary effective population size: A review.

The method to estimate contemporary effective population size (Ne ) based on patterns of linkage disequilibrium (LD) at unlinked loci has been widely applied to natural and managed populations. The underlying model makes many simplifying assumptions, most of which have been evaluated in numerous studies published over the last two decades. Here, these performance evaluations are reviewed and summarized, with a focus on information that facilitates practical application to real populations in nature. Potential sources of bias that are discussed include calculation of r2 (a measure of LD), adjustments for sampling error, physical linkage, age structure, migration and spatial structure, mutation and selection, mating systems, changes in abundance, rare alleles, missing data, genotyping errors, data filtering choices and methods for combining multiple Ne estimates. Factors that affect precision are reviewed, including pseudoreplication that limits the information gained from large genomics datasets, constraints imposed by small samples of individuals, and the challenges in obtaining robust estimates for large populations. Topics that merit further research include the potential to weight r2 values by allele frequency, lump samples of individuals, use genotypic likelihoods rather than called genotypes, prune large LD values and apply the method to species practising partial monogamy.

Adult Attention-Deficit/Hyperactivity Disorder and the Risk of Dementia

Evidence that adult attention-deficit/hyperactivity disorder (ADHD) is associated with an increased risk of dementia is scarce and inconsistent, and potential sources of bias are untested. To examine the association between adult ADHD and the risk of dementia. This prospective national cohort study consisted of 109 218 members of a nonprofit Israeli health maintenance organization born between 1933 and 1952 who entered the cohort on January 1, 2003, without an ADHD or dementia diagnosis and were followed up to February 28, 2020. Participants were aged 51 to 70 years in 2003. Statistical analysis was conducted from December 2022 to August 2023. Adult ADHD was a time-varying covariate, classified as present from the age of the first diagnosis (using the International Classification of Diseases, Ninth Revision, and the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision); otherwise, absent. Cox regression models were fitted to quantify the association between adult ADHD and the risk of incident dementia with hazard ratios (HRs) and their 95% CIs unadjusted and in the primary analysis, using inverse probability weights, adjusted for 18 sources of potential confounding. In 14 complementary analyses, subgroup and sensitivity analyses were implemented. At the beginning of the follow-up, the sample of 109 218 participants had a mean (SD) age of 57.7 (5.5) years, 56 474 participants (51.7%) were female, and 52 744 (48.3%) were male. During follow-up, 730 participants (0.7%) received a diagnosis of adult ADHD, and 7726 (7.1%) received a diagnosis of dementia. Dementia occurred among 96 of 730 participants (13.2%) with adult ADHD and 7630 of 108 488 participants (7.0%) without adult ADHD. In the primary analysis, compared with the absence of adult ADHD, the presence of adult ADHD was statistically significantly (P < .001) associated with an increased dementia risk (unadjusted HR, 3.62 [95% CI, 2.92-4.49; P < .001]; adjusted HR, 2.77 [95% CI, 2.11-3.63; P < .001]). Twelve of the 14 complementary analyses did not attenuate the conclusions based on the results of the primary analysis. There was, however, no clear increase in the risk of dementia associated with adult ADHD among those who received psychostimulant medication, and evidence of reverse causation was mild. In this cohort study of individuals born between 1933 and 1952 and followed up in old age, adult ADHD was associated with an increased risk of dementia. Policy makers, caregivers, patients, and clinicians may wish to monitor reliably for ADHD in old age.

Ranking versus rating in peer review of research grant applications.

The allocation of public funds for research has been predominantly based on peer review where reviewers are asked to rate an application on some form of ordinal scale from poor to excellent. Poor reliability and bias of peer review rating has led funding agencies to experiment with different approaches to assess applications. In this study, we compared the reliability and potential sources of bias associated with application rating with those of application ranking in 3,156 applications to the Canadian Institutes of Health Research. Ranking was more reliable than rating and less susceptible to the characteristics of the review panel, such as level of expertise and experience, for both reliability and potential sources of bias. However, both rating and ranking penalized early career investigators and favoured older applicants. Sex bias was only evident for rating and only when the applicant's H-index was at the lower end of the H-index distribution. We conclude that when compared to rating, ranking provides a more reliable assessment of the quality of research applications, is not as influenced by reviewer expertise or experience, and is associated with fewer sources of bias. Research funding agencies should consider adopting ranking methods to improve the quality of funding decisions in health research.

Complement-activating donor-specific anti-HLA antibodies in solid organ transplantation: systematic review, meta-analysis, and critical appraisal.

Several studies have investigated the impact of circulating complement-activating anti-human leukocyte antigen donor-specific antibodies (anti-HLA DSAs) on organ transplant outcomes. However, a critical appraisal of these studies and a demonstration of the prognostic value of complement-activating status over anti-HLA DSA mean fluorescence intensity (MFI) level are lacking. We conducted a systematic review, meta-analysis and critical appraisal evaluating the role of complement-activating anti-HLA DSAs on allograft outcomes in different solid organ transplants. We included studies through Medline, Cochrane, Scopus, and Embase since inception of databases till May 05, 2023. We evaluated allograft loss as the primary outcome, and allograft rejection as the secondary outcome. We used the Newcastle-Ottawa Scale and funnel plots to assess risk of bias and used bias adjustment methods when appropriate. We performed multiple subgroup analyses to account for sources of heterogeneity and studied the added value of complement assays over anti-HLA DSA MFI level. In total, 52 studies were included in the final meta-analysis (11,035 patients). Complement-activating anti-HLA DSAs were associated with an increased risk of allograft loss (HR 2.77; 95% CI 2.33-3.29, p<0.001; I²=46.2%), and allograft rejection (HR 4.98; 95% CI 2.96-8.36, p<0.01; I²=70.9%). These results remained significant after adjustment for potential sources of bias and across multiple subgroup analyses. After adjusting on pan-IgG anti-HLA DSA defined by the MFI levels, complement-activating anti-HLA DSAs were significantly and independently associated with an increased risk of allograft loss. We demonstrated in this systematic review, meta-analysis and critical appraisal the significant deleterious impact and the independent prognostic value of circulating complement-activating anti-HLA DSAs on solid organ transplant risk of allograft loss and rejection.

Historical sampling error: A neglected factor in long-term biodiversity change research

Long-term time series are increasingly used to assess the effects of global change on plant community diversity and to guide management of target plant communities. However, historical biodiversity data may contain neglected sources of error that can have a significant impact on the results and their interpretation. In our study, we focus on historical sampling error, a source of potential bias in long-term biodiversity assessments that has not been systematically addressed. We resampled two historical datasets of a different origin in the floodplain forests of the Czech Republic, with 534 vegetation plots originally sampled in the 1950s and 1960s. We compared temporal trends in alpha diversity and Ellenberg indicator values (EIVs) between the two parallel surveys. To assess compositional differences, we compared temporal changes in species frequencies. Alpha diversity increased by 9.3 % in one resurvey, but decreased by an average of 30.8 % in the second resurvey. The distribution of EIVs for plots also differed, indicating that each resurvey covered a different part of the environmental gradient. We conclude that preferential historical sampling of the vegetation-environment continuum and species omission may have contributed to the differences in biodiversity and environmental change between the datasets. Our study shows that historical sampling error can have a significant impact on assessments of long-term biodiversity trends. We recommend that historical reference datasets should be critically assessed for potential sources of error in assessments of environmental change and management objectives.

Validity and Reliability of Research Instrument in Evaluation of Work-Based Learning (WBL) Elements

This study focuses on the evaluation of Work-Based Learning (WBL) elements on MTUN (assumed to be a specific institution or program) bachelor students. The objective is to assess the validity and reliability of the research instruments used in the evaluation process. WBL is an educational approach that integrates practical work experiences with academic learning to enhance students' skills and employability. Ensuring the validity of the research instruments is crucial to measure the intended outcomes accurately. Content validity is achieved through careful instrument design, aligning research questions with measures, and seeking expert input. Reliability is established by employing standardized procedures, clear instructions, and rigorous data collection training. This study utilizes a mixed-methods approach, combining quantitative surveys and qualitative interviews, to enhance the validity and reliability of the findings. Limitations and potential sources of bias are acknowledged and addressed. By maintaining validity and reliability in the evaluation, this study aims to provide accurate and trustworthy insights into the effectiveness of WBL elements, contributing to the improvement of WBL programs and enhancing student learning outcomes and career readiness.

Mendelian Randomization Study of Lipid Metabolites Reveals Causal Associations with Heel Bone Mineral Density.

Osteoporosis, which is a bone disease, is characterized by low bone mineral density and an increased risk of fractures. The heel bone mineral density is often used as a representative measure of overall bone mineral density. Lipid metabolism, which includes processes such as fatty acid metabolism, glycerol metabolism, inositol metabolism, bile acid metabolism, carnitine metabolism, ketone body metabolism, sterol and steroid metabolism, etc., may have an impact on changes in bone mineral density. While some studies have reported correlations between lipid metabolism and heel bone mineral density, the overall causal relationship between metabolites and heel bone mineral density remains unclear. to investigate the causal relationship between lipid metabolites and heel bone mineral density using two-sample Mendelian randomization analysis. Summary-level data from large-scale genome-wide association studies were extracted to identify genetic variants linked to lipid metabolite levels. These genetic variants were subsequently employed as instrumental variables in Mendelian randomization analysis to estimate the causal effects of each lipid metabolite on heel bone mineral density. Furthermore, metabolites that could potentially be influenced by causal relationships with bone mineral density were extracted from the KEGG and WikiPathways databases. The causal associations between these downstream metabolites and heel bone mineral density were then examined. Lastly, a sensitivity analysis was conducted to evaluate the robustness of the results and address potential sources of bias. A total of 130 lipid metabolites were analyzed, and it was found that acetylcarnitine, propionylcarnitine, hexadecanedioate, tetradecanedioate, myo-inositol, 1-arachidonoylglycerophosphorine, 1-linoleoylglycerophoethanolamine, and epiandrosterone sulfate had a causal relationship with heel bone mineral density (p < 0.05). Furthermore, our findings also indicate an absence of causal association between the downstream metabolites associated with the aforementioned metabolites identified in the KEGG and WikiPathways databases and heel bone mineral density. This work supports the hypothesis that lipid metabolites have an impact on bone health through demonstrating a causal relationship between specific lipid metabolites and heel bone mineral density. This study has significant implications for the development of new strategies to osteoporosis prevention and treatment.

Which people are most affected by changes to data linkage methodology? An exploration of patient, organisational and spatiotemporal characteristics in administrative hospital data in England

ObjectivesIn 2021, NHS Digital changed the process used to link records belonging to the same person across and within data collections. Our objectives were to identify patient, organisational and spatiotemporal characteristics associated with records impacted by this change and the implications for researchers using this data.&#x0D; MethodsWe used an observational cohort study of patients, aged 55 or less, with a secondary care contact recorded in any of the NHS Digital (now part of NHS England) curated Hospital Episode Statistics (HES) datasets between April 1997 and March 2021. We compared clusters of records assigned to each patient using the HES ID (old methodology using a three-step deterministic algorithm) and the Person ID (new methodology using a master patient spine). We used multivariable logistic regression to identify patient, organisational and spatiotemporal (such as area-level deprivation and year of first contact) characteristics associated with patients whose cluster had changed.&#x0D; ResultsOf 88 million hospital records in 2019, there were 18,968,711 distinct HES IDs and 18,717,142 distinct TPIs. Of the 12,701,169 HES IDs with more than one record, 145,948 (1.1%) were split into multiple Person IDs. Of the 12,999,671 Person IDs with more than one record, 483,091 (3.7%) were associated with two or more merged HES IDs. We will present an analysis using data covering the period April 1997 to March 2021 - 1.25 billion records - and present the characteristics associated with changes between linkage methods.&#x0D; ConclusionOur findings indicate that this change consolidated clusters, resulting in fewer distinct individuals in the data. Our findings will inform researchers about which groups of individuals are most likely to be affected by changes to linkage methodology. This is vital for understanding potential sources of bias due to linkage error.

Effect of blastocyst shrinkage on assisted reproductive outcomes: a retrospective cohort study describing a new morphological evaluation of blastocyst pre-vitrification and post-warming

BackgroundThe failure of frozen-thawed blastocysts to re-expand adequately within a few hours after warming has been reported to have a negative impact on assisted reproductive technology (ART) outcomes. However, the extent to which this failure truly affects ART outcomes has not yet been presented in a manner that is easily understandable to medical practitioners and patients. This study aimed to assess the effects of blastocyst shrinkage on ART outcomes and determine a more effective morphological evaluation approach for use in clinical settings.MethodsThis retrospective observational cohort study of frozen-thawed blastocyst transfer cycles was conducted from April 2017 to March 2022. Overall, 1,331 cycles were eligible for inclusion, of which 999 were good-quality blastocysts (GQB) and 332 were non-good-quality blastocysts (non-GQB). All frozen-thawed blastocyst transfer cycles performed during the specified study period were included in the study. Exclusion criteria were established to mitigate potential sources of bias as these cycles could impact implantations. We calculated rates and age-adjusted odds ratios of implantation, clinical pregnancy, ongoing pregnancy, and live birth of the re-expansion group, which showed sufficient expansion, and shrinkage group, which showed insufficient expansion. We also calculated the implantation, clinical pregnancy, ongoing pregnancy, and live birth rates of the re-expansion and shrinkage groups for each morphological scoring system parameter.ResultsA reduced ART outcome was observed with use of blastocysts with shrinkage after vitrification/warming. The age-adjusted odds ratios for implantation, clinical pregnancy, ongoing pregnancy, and live birth were lower in the shrinkage group than in the re-expansion group.ConclusionsThis study examined the adverse effect of blastocyst shrinkage after warming and recovery culturing on reproductive outcomes in a clinically useful manner by retrospectively examining a substantial number of frozen-thawed embryo transfer cycles. The study findings can possibly reduce concerns regarding over- or under-estimation of blastocyst implantation by allowing providers and patients to refer to the data.