Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) by Gerald McGwin, Linda M Zangwill, Nicholas Evans, Shannon McWeeney, Cecilia S Lee, Bhavesh Patel, Jeffrey C Edberg, Cynthia Owsley, Aaron Lee, Cecilia Lee, Sally L Baxter, Michael Snyder, Samantha Hurst, Nicole Ehrhardt, Christopher Chute, Dawn S Matthies, Julia P Owen, Amir Bahmani, Sally Baxter, Edward Boyko, Aaron Cohen, Jorge Contreras, Garrison Cottrell, Virginia de Sa, Jeffrey Edberg, Irl Hirsch, Michelle Hribar, T.Y. Alvin Liu, Bonnie Maldenado, Sara Singer, Bradley Voytek, Joseph Yracheta, Linda Zangwill

“…The fundamental rationale for promoting health resilience in T2DM stems from its high prevalence of 10.5% of the world’s adult population and its contribution to many adverse health events.Methods AI-READI is a cross-sectional study whose target enrollment is 4000 people aged 40 and older, triple-balanced by self-reported race/ethnicity (Asian, black, Hispanic, white), T2DM (no diabetes, pre-diabetes and lifestyle-controlled diabetes, diabetes treated with oral medications or non-insulin injections and insulin-controlled diabetes) and biological sex (male, female) (Clinicaltrials.org approval number STUDY00016228). …”
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Prevalence of AZFс Y chromosome microdeletions and association with spermatogenesis in Russian men from the general population by L. V. Osadchuk, G. V. Vasiliev, M. K. Ivanov, M. A. Prasolova, M. A. Kleshchev, A. V. Osadchuk

“…The aim of the present study was to assess the frequency of various types of AZFc microdeletions and to search for associations with spermatogenesis parameters in men of Slavic ethnicity from the general Russian population (n = 700, average age 25.8 years). …”
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Physical behaviours during pregnancy may alter the association of maternal insulin sensitivity with neonatal adiposity: a prospective pre-birth cohort of mother-child pairs by Piraveena Satkunanathan, Catherine Allard, Myriam Doyon, Patrice Perron, Luigi Bouchard, Marie-France Hivert, Tricia M. Peters

“…Linear regression analyses assessed the association of insulin sensitivity with birthweight z-score and sum of neonatal skinfold thickness, adjusting for maternal age, race/ethnicity, gravidity, smoking, with and without adjustment for maternal body mass index at V1. …”
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Distribution of air quality health benefits of medium and heavy-duty electrification policies in New York City by Brian Naess, Jonathan Buonocore, Veronica Southerland, Muskaan Khemani, Catherine Seppanen, Ananya Roy, Frederica Perera, Kaitlyn E Coomes, Rick Rykowski, Saravanan Arunachalam

“…The objective of this study is to evaluate the distribution of air quality health benefits of Medium- and Heavy-Duty Electric Vehicle (MHDEV) policies in New York City (NYC), quantifying differences across neighborhoods (census tracts) and population subgroups (race, ethnicity). We ran an integrated model for a 2040 baseline/business-as-usual scenario and for two policy scenarios simulating different rates of MHDEV adoption, also for 2040. …”
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Impact of Flexor Tendon Traction Tenolysis on Clinical Outcomes in Open A1 Pulley Release for Trigger Finger by Shalimar Abdullah, BMBS, MS, Syed Jeffrey Syed Ahmad Kabeer, MBChB, MD, Lim Chia Hua, MD, Jamari Sapuan, MD, MS, Parminder Singh Gill, MBBS, MS, Elaine Soh Zi Fan, MBBS, MD

“…Baseline characteristics, including age, gender (female/male), ethnicity, occupational status, diabetes status, and trigger finger severity, were comparable between the two groups. …”
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Assessment of vitamin D status in obese and non-obese patients: A case-control study by Salma Derbel, Lamiae Zarraa, Imane Assarrar, Nisrine Bouichrat, Siham Rouf, Hanane Latrech

“…Controls (n = 60) were randomly assigned and were matched for age, sex, glycated hemoglobin, ethnicity, and geographic area. The levels of Vitamin D in the serum were determined in obese patients and non-obese controls. …”
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Behavioral risk factors and socioeconomic inequalities in ischemic heart disease mortality in the United States: A causal mediation analysis using record linkage data. by Yachen Zhu, Laura Llamosas-Falcón, William C Kerr, Jürgen Rehm, Charlotte Probst

“…Analyses were performed stratified by sex and adjusted for marital status, race and ethnicity, and survey year. In both males and females, clear socioeconomic gradients in IHD mortality were observed, with low- and middle-education people bearing statistically significantly higher risks compared to high-education people. …”
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Risk of SARS-CoV-2 infection before and after the Omicron wave in a cohort of healthcare workers in Ontario, Canada by Jorge L. Martinez-Cajas, Ann Jolly, Yanping Gong, Gerald Evans, Santiago Perez-Patrigeon, Bradley Stoner, T. Hugh Guan, Beatriz Alvarado

“…We employed Poisson regression for period #1 and Cox regression for period #2 to examine associations of demographic factors (age, sex, ethnicity, migration status, income insufficiency), health factors (chronic conditions, smoking history, SARS-CoV-2 vaccination status), household factors (exposure to COVID-19), occupational factors (work role, exposure to COVID-19 patients, personal protective equipment access, aerosol-generating procedures) and community exposures (use of masks, distance, hand-washing) with SARS-CoV-2 infection. …”
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How intravitreal anti-vascular endothelial growth factor initial dosing impacts patient outcomes in diabetic macular oedema by Rishi P. Singh, David Tabano, Blanche L. Kuo, Andrew LaPrise, Theodore Leng, Eunice Kim, Meghan Hatfield, Vincent Garmo

“…Conclusions Various sociodemographic factors associate with initial anti-VEGF doses, including race, ethnicity and insurance. Although eyes with frequent initial doses maintained higher VA than those without, they also receive more injections over time. …”
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Longitudinal associations between air pollution and incident dementia as mediated by MRI-measured brain volumes in the UK Biobank by Rhiannon Thompson, Xinning Tong, Xueyi Shen, Jinjun Ran, Shengzhi Sun, Xiaoxin Iris Yao, Chen Shen

“…Associations between environmental exposures, brain volumes, and dementia cases (diagnosed post-MRI) were tested using linear and logistic regression and adjusted for age, sex, household income, ethnicity, education, smoking, and area-level deprivation. …”
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Nicotine dependence is associated with an increased risk of developing chronic, non-communicable inflammatory disease: a large-scale retrospective cohort study by Khalaf Kridin, Khalaf Kridin, Khalaf Kridin, Cristian Papara, Katja Bieber, David A. De Luca, Jan Philipp Klein, Marlene A. Ludwig, Philip Curman, Philip Curman, Philip Curman, Philip Curman, Artem Vorobyev, Astrid Dempfle, Ralf J. Ludwig

“…Using propensity score matching (PSM) to control for age, sex, ethnicity, and other CID risk factors, we contrasted the risk of developing any or any of the 38 CIDs in 881,192 EHRs from individuals with nicotine dependence to PSM-matched unexposed counterparts.ResultsThe analytical pipeline was validated by demonstrating an increased risk of individuals exposed to nicotine dependence for subsequent diagnosis of myocardial infarction, malignant neoplasm of the lung, and chronic obstructive pulmonary disease. …”
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Effect of nirmatrelvir/ritonavir (Paxlovid) on hospitalization among adults with COVID-19: An electronic health record-based target trial emulation from N3C. by Abhishek Bhatia, Alexander J Preiss, Xuya Xiao, M Daniel Brannock, G Caleb Alexander, Robert F Chew, Hannah Davis, Megan Fitzgerald, Elaine Hill, Elizabeth P Kelly, Hemalkumar B Mehta, Charisse Madlock-Brown, Kenneth J Wilkins, Christopher G Chute, Melissa Haendel, Richard Moffitt, Emily R Pfaff, N3C Consortium

“…To emulate randomization, we used the clone-censor-weight technique with inverse probability of censoring weights to balance a set of covariates including sex, age, race and ethnicity, comorbidities, community well-being index (CWBI), prior healthcare utilization, month of COVID-19 index, and site of care provision. …”
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A Digital Tool for Clinical Evidence–Driven Guideline Development by Studying Properties of Trial Eligible and Ineligible Populations: Development and Usability Study by Shahzad Mumtaz, Megan McMinn, Christian Cole, Chuang Gao, Christopher Hall, Magalie Guignard-Duff, Huayi Huang, David A McAllister, Daniel R Morales, Emily Jefferson, Bruce Guthrie

“…ObjectiveThis study aims to develop a digital tool that characterizes the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease or condition regarding demography (eg, age, gender, ethnicity), comorbidity, coprescription, hospitalization, and mortality. …”
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The Relationship between Intramuscular Adipose Tissue, Functional Mobility, and Strength in Postmenopausal Women with and without Type 2 Diabetes by Janet M. Pritchard, Sarah Karampatos, Karen A. Beattie, Lora M. Giangregorio, George Ioannidis, Stephanie A. Atkinson, Lehana Thabane, Hertzel Gerstein, Zubin Punthakee, Jonathan D. Adachi, Alexandra Papaioannou

“…Regression was used to determine associations between the following: (1) type 2 diabetes and IntraMAT (covariates: age, ethnicity, BMI, waist : hip ratio, and energy expenditure), (2) IntraMAT and TUG (covariates: diabetes, age, BMI, and energy expenditure), and (3) IntraMAT and grip strength (covariates: diabetes, age, height, and lean mass). …”
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Adapting the Technology Acceptance Model to Examine the Use of Information Communication Technologies and Loneliness Among Low-Income, Older Asian Americans: Cross-Sectional Survey... by Pauline DeLange Martinez, Daniel Tancredi, Misha Pavel, Lorena Garcia, Heather M Young

“…English proficiency and education positively predicted PEOU (r=0.25, P<.001; r=0.26, P<.001) and ICT use (β=1.66, P=.03; β=.21, P<.001), while subjective cognitive decline and Asian ethnicity were positively associated with loneliness (β=.31, P<.001; β=.25, P<.001). …”
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Painting a portrait: Analysis of national health survey data for cancer genetic counseling by Monica H. Stamp, Ora K. Gordon, Christopher P. Childers, Kimberly K. Childers

“…On bivariate analysis, there were significant differences in proportions undergoing genetic counseling by sex, race/ethnicity, insurance, citizenship, education, age, and cancer status (P < 0.01). …”
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Systematic review and meta‐analysis of the pelvic organ prolapse and vaginal prolapse among the global population by Tharanga Mudalige, Vindya Pathiraja, Gayathri Delanerolle, Heitor Cavalini, Shuqi Wu, Julie Taylor, Om Kurmi, Kathryn Elliot, Sharron Hinchliff, Carol Atkinson, Kristina Potocnik, Paula Briggs, Lucky Saraswat, Helen Felicity Kemp, George Eleje, Toh Teck Hock, Cristina Laguna Benetti‐Pinto, Irfan Muhammad, Rabia Kareem, Yassine Bouchareb, Peter Phiri, Ruishu Zhang, Yunfei Weng, Ieera Aggarwal, Jian Qing Shi, Ashish Shetty, Ian Litchfield, Nirmala Rathnayake, Sohier Elneil

“…The review highlighted a lack of diversity in terms of ethnicity, age and comorbidity status, which are essential to fully understanding the impact of POP. …”
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Personalised Lung Cancer Screening (PLuS) study to assess the importance of coexisting chronic conditions to clinical practice and policy: protocol for a multicentre observational... by Yi Guo, Dongyu Zhang, Rafael Meza, Jihyoun Jeon, Dejana Braithwaite, Gerard A Silvestri, Michael K Gould, Jiang Bian, Bruno Hochhegger, Shama D Karanth, Christopher G Slatore, Martin Tammemagi, Mattthew Schabath, Meghann Wheeler, Frederic J Kaye

“…Data will be integrated into a unified repository to (1) examine the burden of multimorbidity by race/ethnicity, socioeconomic status and age; (2) quantify potential benefits and harms; and (3) use the observational data with validated simulation models in the Cancer Intervention and Surveillance Modeling Network (CISNET) to provide LCS outcomes in the real-world US population. …”
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Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review by Maxime Sasseville, Steven Ouellet, Caroline Rhéaume, Malek Sahlia, Vincent Couture, Philippe Després, Jean-Sébastien Paquette, David Darmon, Frédéric Bergeron, Marie-Pierre Gagnon

“…The most frequently investigated protected attributes were race (or ethnicity), examined in 12 of the 17 studies, and sex (often identified as gender), typically classified as “male versus female” in 10 of the studies. …”
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Prediction of Distant Metastasis of Renal Cell Carcinoma Based on Interpretable Machine Learning: A Multicenter Retrospective Study by Dong J, Duan M, Liu X, Li H, Zhang Y, Zhang T, Fu C, Yu J, Hu W, Peng S

“…Performance for the prediction model was also well performed in different sub-cohort stratified by age, gender, and ethnicity. The calibration curve indicated that the predicted values are highly consistent with the actual observed values. …”
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