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Background: Data sharing improves the value, synthesis, and integrity of research, but rates are low. Data sharing might be improved if data sharing policies were prominent and actionable at every stage of research. We aimed to systematically describe the epidemiology of data sharing policies across the health research lifecycle. Methods: This was a cross‐sectional analysis of the data sharing policies of the largest health research funders, all national ethics committees, all clinical trial registries, the highest‐impact medical journals, and all medical research data repositories. Stakeholders' official websites, online reports, and other records were reviewed up to May 2022. The strength and characteristics of their data sharing policies were assessed, including their policies on data sharing intention statements (a.k.a. data accessibility statements) and on data sharing specifically for coronavirus disease studies. Data were manually extracted in duplicate, and policies were descriptively analysed by their stakeholder and characteristics. Results: Nine hundred and thirty‐five eligible stakeholders were identified: 110 funders, 124 ethics committees, 18 trial registries, 273 journals, and 410 data repositories. Data sharing was required by 41% (45/110) of funders, no ethics committees or trial registries, 19% (52/273) of journals and 6% (24/410) of data repositories. Among funder types, a higher proportion of private (63%, 35/55) and philanthropic (67%, 4/6) funders required data sharing than public funders (12%, 6/49). Conclusion: Data sharing requirements, and even recommendations, were insufficient across health research. Where data sharing was required or recommended, there was limited guidance on implementation. We describe multiple pathways to improve the implementation of data sharing. Public funders and ethics committees are two stakeholders with particularly important untapped opportunities. [ABSTRACT FROM AUTHOR]

This article presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses an object-agnostic grasping framework to map from visual observations to actions: inferring dense pixel-wise probability maps of the affordances for four different grasping primitive actions. It then executes the action with the highest affordance and recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional data collection or re-training. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT–Princeton Team system that took first place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu/ [ABSTRACT FROM AUTHOR]

Background: Contrast-induced nephropathy is a prevalent cause of hospital-acquired renal insufficiency and increases adverse events in older patients undergoing angiography and percutaneous coronary intervention. The Mehran risk score has been widely used in Vietnam to assess contrast-induced nephropathy risk in patients before coronary angiography and percutaneous coronary intervention. Recently, there has been a shift toward the adoption of simpler risk prediction models, such as the contrast volume-to-glomerular filtration rate ratio. This study aimed to (1) determine the incidence of contrast-induced nephropathy in older patients undergoing coronary angiography and/or percutaneous coronary intervention, and (2) compare the validity of the contrast volume-to-glomerular filtration rate ratio and the Mehran score in predicting contrast-induced nephropathy., Method: This is a prospective cohort study conducted at a hospital in Vietnam from September 2019 to May 2020. Consecutive patients aged ⩾60 years who underwent coronary angiography and/or percutaneous coronary intervention were recruited. The contrast volume-to-glomerular filtration rate ratio and the Mehran score were evaluated for their predictive utility regarding contrast-induced nephropathy risk. The receiver operator characteristic was employed to calculate the area under the curve for both the contrast volume-to-glomerular filtration rate ratio and the Mehran score in predicting contrast-induced nephropathy., Results: The study included 170 participants with a mean age of 70 years and 33.1% were female. Contrast-induced nephropathy was diagnosed in 9.4% of the participants. Participants with contrast-induced nephropathy exhibited a higher prevalence of chronic kidney disease, anemia, and heart failure. There was no significant difference between the area under the curves of the contrast volume-to-glomerular filtration rate ratio (0.79, 95% CI: 0.65-0.92), and the Mehran score (0.65, 95% CI: 0.51-0.82) in predicting contrast-induced nephropathy., Conclusion: Our findings indicate that contrast-induced nephropathy was prevalent among older patients following percutaneous coronary intervention. The contrast volume-to-glomerular filtration rate ratio demonstrated a good prognostic value for predicting contrast-induced nephropathy comparable to that of the Mehran score. Further research is needed to identify optimal cutoff values for the contrast volume-to-glomerular filtration rate ratio in older patients., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Background: Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication., Methods and Results: MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302 515 individuals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740-0.806) and 0.759 (95% CI: 0.726-0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139-0.140), P < 0.0001., Conclusion: ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention.This review was registered with PROSPERO (CRD42020220811)., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)