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"A scoping review to map instruments used to assess distress in T2 diabetes." Screening for distress in people living with diabetes could potentially identify which stressors could be ameliorated by interventions, using valid and reliable measures is important. This review will map and identify the available validated measures of distress used in the field of type 2 diabetes. Using a validated instrument to assess distress is one strategy healthcare providers can use to identify distress in the clinical setting and develop structured plans to address specific needs.

Supplemental Material for Remote consultations in primary care across low-, middle- and high-income countries: Implications for policy and care delivery by Siân Williams, Amanda Barnard, Phil Collis, Jaime Correia de Sousa, Suraj Ghimire, Monsur Habib, Tessa Jelen, Frank Kanniess, Vince Mak, Sonia Martins, Ema Paulino, Hilary Pinnock, Miguel Roman, Hanna Sandelowsky, Ioanna Tsiligianni, Laurine van der Steen and Fabio Weber Donatelli in the Journal of Health Services Research & Policy.

Additional file 3. Table S1. Comparison of the identified barriers and facilitators between low/middle- and high-income countries. Table S2. Comparison of the identified barriers and facilitators between male and female youth

Additional file 2. Additional tables.

Additional file 1. Table S1. Search strategy in MEDLINE. Table S2. Critical appraisal results of included quasi-experimental studies (quantitative studies). Table S3. Critical appraisal results of included randomized controlled trial (quantitative study). Table S4. Critical appraisal results of included qualitative studies and mixed methods study (qualitative component).

Additional file 1.

Black box models only provide results for deep learning tasks, and lack informative details about how these results were obtained. Knowing how input variables are related to outputs, in addition to why they are related, can be critical to translating predictions into laboratory experiments, or defending a model prediction under scrutiny. In this paper, we propose a general theory that defines a variance tolerance factor (VTF) inspired by influence function, to interpret features in the context of black box neural networks by ranking the importance of features, and construct a novel architecture consisting of a base model and feature model to explore the feature importance in a Rashomon set that contains all well-performing neural networks. Two feature importance ranking methods in the Rashomon set and a feature selection method based on the VTF are created and explored. A thorough evaluation on synthetic and benchmark datasets is provided, and the method is applied to two real world examples predicting the formation of noncrystalline gold nanoparticles and the chemical toxicity 1793 aromatic compounds exposed to a protozoan ciliate for 40 hours.

Additional file 4: Table D. Descriptive statistics of audit findings by year. *health record

Authors’ original file for figure 3

Additional file 2: Table B. Results for questions relating to knowledge of delirium

Additional file 3: Table C. Results for questions relating to risk of delirium

Authors’ original file for figure 1

Supplemental material, sj-pdf-1-vdi-10.1177_1040638721990333 for Evaluation of a real-time RT-PCR panel for detection of SARS-CoV-2 in bat guano by Eman Anis, Greg Turner, Julie C. Ellis, Andrew Di Salvo, Amanda Barnard, Susan Carroll and Lisa Murphy in Journal of Veterinary Diagnostic Investigation

Additional file 1: Table A. Results for rating scales commonly used to detect certain conditions for cognitive impairment, delirium, dementia, depression or none. *Chi square test

Authors’ original file for figure 2

Although nanoparticles have been shown to have clear technological advantages, their use in some consumer products remains controversial, particularly where these products come in direct contact with our bodies. There has been much discussion about using metal oxide nanoparticles in sunscreens, and numerous technology assessments aimed at predicting the type, size and concentration of nanoparticles and surface treatments that will be best for consumers. Yet, the optimal configuration is ultimately the one that people actually want and are willing to pay for, but until now consumer preferences have not been included in model predictions. We describe and discuss a proof of concept study in which we design and implement a hypothetical sunscreen product configurator to predict how people tradeoff sun protection factor (SPF), product transparency and potential toxicity from reactive oxygen species (ROS) in configuring their most preferred sunscreen. We also show that preferred nanoparticle sizes and concentrations vary across demographic groups. Our results suggest that while consumers choose to reduce or eliminate potential toxicity when possible, they do not automatically sacrifice high SPF and product transparency to avoid the possibility of toxicity from ROS. We discuss some advantages of using product configurators to study potential product designs and suggest some future research possibilities Refereed/Peer-reviewed