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Background: The provision of graded supervision affording progressive autonomy is fundamental to the progression of a medical learner toward competency for independent practice; the decision of how much supervision versus autonomy to provide a trainee in the execution of clinical care constitutes an entrustment decision. Despite entrustment decision making occurring both daily in practice and summatively at points of matriculation through stages of medical training, the factors influencing entrustment decisions remain poorly understood across clinical contexts. Objective: This study was designed to explore the central research question: How are entrustment decisions made in the medical intensive care unit (ICU)? Methods: This qualitative case study used semistructured interviews with attending pulmonary and critical care physicians in the medical ICU at a major midwestern medical center to explore the entrustment decision-making process as it was enacted in the clinical environment. Results: Five major themes emerged from the data: 1) task, circumstance, and trainee factors contribute to entrustment decision making; 2) ad hoc entrustment decisions are enacted by supervisors with a consideration of the care team as a unit, not only an individual; 3) autonomy does not only arise out of entrustment, but outcomes of prior autonomous actions by the trainee inform the intention to entrust; 4) entrustment decision making includes a social process of back-and-forth akin to negotiation; and 5) entrustment is a learned skill. Conclusion: The process of entrustment decision making in the ICU is more complex than prior frameworks have captured; a model with more complete incorporation of the factors that influence entrustment in the ICU is presented. It is not clear how often ad hoc entrustment decisions in clinical practice are primarily driven by factors pertaining directly to trainee competence, which carries implications in the use of entrustment for assessment. [ABSTRACT FROM AUTHOR]

Body mass index (BMI; weight (kg)/height (m)2) is commonly used to measure general adiposity. However, evidence of its appropriateness for males and females remains inconsistent. We aimed to identify the most appropriate sex-specific power value that height should be raised to in the formula and the value that would make it achieve height independency and body fatness dependency. We randomly assigned UK Biobank participants recruited in the United Kingdom between 2006 and 2010 (n  = 489,873; mean age = 56.5 years; 94.2% White) to training and testing sets (80%:20%). Using height raised to the power of −50.00 to 50.00, we identified the optimal power value that either minimized correlation with height or maximized correlation with body fat percentage, using age-adjusted correlations. The optimal power values for height were 1.77 for males and 1.39 for females. The new formulas resulted in 4.5% of females and 2.4% of males being reclassified into a different BMI category. The formulas did not show significant improvement (in terms of area under the receiver operating characteristic curve, sensitivity, and specificity) in identifying individuals with excessive body fat percentage or in predicting risk of all-cause mortality. Therefore, the conventional BMI formula is still valuable in research and disease screening for both sexes. [ABSTRACT FROM AUTHOR]

Objectives: To investigate associations between health-related behaviors as measured using the Brain Care Score (BCS) and neuroimaging markers of white matter injury., Methods: This prospective cohort study in the UK Biobank assessed the BCS, a novel tool designed to empower patients to address 12 dementia and stroke risk factors. The BCS ranges from 0 to 21, with higher scores suggesting better brain care. Outcomes included white matter hyperintensities (WMH) volume, fractional anisotropy (FA), and mean diffusivity (MD) obtained during 2 imaging assessments, as well as their progression between assessments, using multivariable linear regression adjusted for age and sex., Results: We included 34,509 participants (average age 55 years, 53% female) with no stroke or dementia history. At first and repeat imaging assessments, every 5-point increase in baseline BCS was linked to significantly lower WMH volumes (25% 95% CI [23%-27%] first, 33% [27%-39%] repeat) and higher FA (18% [16%-20%] first, 22% [15%-28%] repeat), with a decrease in MD (9% [7%-11%] first, 10% [4%-16%] repeat). In addition, a higher baseline BCS was associated with a 10% [3%-17%] reduction in WMH progression and FA decline over time., Discussion: This study extends the impact of the BCS to neuroimaging markers of clinically silent cerebrovascular disease. Our results suggest that improving one's BCS could be a valuable intervention to prevent early brain health decline.

Background: Bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) serves as common modalities for body composition assessment. This study was aimed to evaluate the agreement between BIA and DXA measures in UK Biobank., Methods: UK Biobank participants with body fat mass (FM) and fat-free mass (FFM) estimates obtained through BIA (Tanita BC418MA) and DXA concurrently were included. Correlation between BIA and DXA-derived estimates were assessed with Lin's concordance correlation coefficients. Bland-Altman and Passing-Boblok analyses were performed to quantify the difference and agreement between BIA and DXA. Multivariable linear regression was used to identify predictors influencing the differences. Finally, prediction models were developed to calibrate BIA measures against DXA., Results: The analysis included 34437 participants (female 51.4%, mean age 64.1 years at imaging assessment). BIA and DXA measurements were highly correlated (Lin's concordance correlation coefficient 0.94 for FM and 0.94 for FFM). BIA (Tanita BC418MA) underestimates FM overall by 1.84 kg (23.77 vs. 25.61, p < 0.01), and overestimated FFM overall by 2.56 kg (52.49 vs. 49.93, p < 0.01). The BIA-DXA differences were associated with FM, FFM, BMI and waist circumference. The developed prediction models showed overall good performance in calibrating BIA data., Conclusion: Our analysis exhibited strong correlation between BIA (Tanita BC418MA)- and DXA-derived body composition measures at a population level in UK Biobank. However, the BIA-DXA differences were observed at individual level and associated with individual anthropometric measures. Future studies may explore the use of prediction models to enhance the calibration of BIA measures for more accurate assessments in UK Biobank., Competing Interests: Declaration of competing interest There is no conflict of interest., (Copyright © 2024. Published by Elsevier Ltd.)