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The novel coronavirus (COVID-19) is a highly contagious disease responsible for millions of deaths worldwide. Effective vaccines against COVID-19 are now available, however, an extreme form of vaccine hesitancy known as anti-vax attitudes challenge vaccine acceptance and distribution efforts. To understand these anti-vax attitudes and their associated psychological characteristics, we examined several predictors of vaccine hesitancy for COVID-19 and anti-vax attitudes generally. We surveyed 1004 adults (M = 47.0 years, SD = 17.1 years, range 18–98 years) in September-October 2020 across the United States (51% female, 49% male; 76.5% White, 23.5% non-White), prior to widespread availability of the COVID-19 vaccines. Attitudes toward vaccinations were influenced by a variety of factors, especially political attitudes. We should therefore anticipate and attempt to mitigate these challenges to achieving widespread vaccination to reduce the spread of COVID-19 and other communicable diseases. [ABSTRACT FROM AUTHOR]

It remains unclear how the seemingly ubiquitous use of the internet impacts user's offline personal relationships, particularly those that are romantic or sexual. Therefore, we conducted a national online survey to better understand the associations among internet use, sexual behavior, and adjustment called the Sexual Behaviors, Internet Use, and Psychological Adjustment Survey (SIPS). Here, we report patterns of sexual behavior in a sample of adults (N = 1987; ages 18–70) in the United States to establish its representativeness and consistency with similar recent surveys. We found age- and sex-related trends in oral, vaginal, and anal sex in terms of prevalence, frequency, number of partners, and age of initiation consistent with prior studies. We also detected differences in sexual behaviors based on relationship status and sexual orientation, but small and relatively few significant differences across racial and ethnic groups. The results confirm and expand upon trends identified in prior national surveys of sexual behavior, establishing the representativeness of the SIPS sample for use in future research examining the links among sexual behaviors and romantic relationships, internet use, and adjustment. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation in small internal cohorts, without external validation or data on real-world clinical utility. We developed a strategy for the clinical validation of deep learning models for segmenting primary non-small-cell lung cancer (NSCLC) tumours and involved lymph nodes in CT images, which is a time-intensive step in radiation treatment planning, with large variability among experts., Methods: In this observational study, CT images and segmentations were collected from eight internal and external sources from the USA, the Netherlands, Canada, and China, with patients from the Maastro and Harvard-RT1 datasets used for model discovery (segmented by a single expert). Validation consisted of interobserver and intraobserver benchmarking, primary validation, functional validation, and end-user testing on the following datasets: multi-delineation, Harvard-RT1, Harvard-RT2, RTOG-0617, NSCLC-radiogenomics, Lung-PET-CT-Dx, RIDER, and thorax phantom. Primary validation consisted of stepwise testing on increasingly external datasets using measures of overlap including volumetric dice (VD) and surface dice (SD). Functional validation explored dosimetric effect, model failure modes, test-retest stability, and accuracy. End-user testing with eight experts assessed automated segmentations in a simulated clinical setting., Findings: We included 2208 patients imaged between 2001 and 2015, with 787 patients used for model discovery and 1421 for model validation, including 28 patients for end-user testing. Models showed an improvement over the interobserver benchmark (multi-delineation dataset; VD 0·91 [IQR 0·83-0·92], p=0·0062; SD 0·86 [0·71-0·91], p=0·0005), and were within the intraobserver benchmark. For primary validation, AI performance on internal Harvard-RT1 data (segmented by the same expert who segmented the discovery data) was VD 0·83 (IQR 0·76-0·88) and SD 0·79 (0·68-0·88), within the interobserver benchmark. Performance on internal Harvard-RT2 data segmented by other experts was VD 0·70 (0·56-0·80) and SD 0·50 (0·34-0·71). Performance on RTOG-0617 clinical trial data was VD 0·71 (0·60-0·81) and SD 0·47 (0·35-0·59), with similar results on diagnostic radiology datasets NSCLC-radiogenomics and Lung-PET-CT-Dx. Despite these geometric overlap results, models yielded target volumes with equivalent radiation dose coverage to those of experts. We also found non-significant differences between de novo expert and AI-assisted segmentations. AI assistance led to a 65% reduction in segmentation time (5·4 min; p<0·0001) and a 32% reduction in interobserver variability (SD; p=0·013)., Interpretation: We present a clinical validation strategy for AI models. We found that in silico geometric segmentation metrics might not correlate with clinical utility of the models. Experts' segmentation style and preference might affect model performance., Funding: US National Institutes of Health and EU European Research Council., Competing Interests: Declaration of interests AH reports consultancy fees for Altis Labs; and owning stock in Altis Labs. HJWLA reports being a scientific adviser and shareholder for Onc.Ai, Love Health, Health-AI, and Bristol Myers Squibb. RHM reports a research grant from ViewRay; honoraria from NewRT and ViewRay; is on an advisory board for AstraZeneca; and reports travel expenses from NewRT. DEK reports honoraria from RefleXion; and is a consultant and on an advisory board for Genentech. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)