Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

The recommended first-line treatment for insomnia is cognitive behavioral therapy for insomnia (CBTi), but access is limited. Telehealth- or internet-delivered CBTi are alternative ways to increase access. To date, these intervention modalities have never been compared within a single study. Further, few studies have examined a) predictors of response to the different modalities, b) whether successfully treating insomnia can result in improvement of health-related biomarkers, and c) mechanisms of change in CBTi. This protocol was designed to compare the three CBTi modalities to each other and a waitlist control for adults aged 50-65 years (N = 100). Participants are randomly assigned to one of four study arms: in-person- (n=30), telehealth- (n=30) internet-delivered (n=30) CBTi, or 12-week waitlist control (n=10). Outcomes include self-reported insomnia symptom severity, polysomnography, circadian rhythms of activity and core body temperature, blood- and sweat-based biomarkers, cognitive functioning, and magnetic resonance imaging.

We examined the association between influenza vaccination policies at acute care hospitals and influenza vaccination coverage among healthcare personnel for the 2021–22 influenza season. Mandatory vaccination and masking for unvaccinated personnel were associated with increased odds of vaccination. Hospital employees had higher vaccination coverage than licensed independent practitioners.

Psychopathology assessed across the lifespan often can be summarized with a few broad dimensions: internalizing, externalizing, and psychosis/thought disorder. Extensive overlap between internalizing and externalizing symptoms has garnered interest in bifactor models comprised of a general co-occurring factor and specific internalizing and externalizing factors. We focus on internalizing and externalizing symptoms and compare a bifactor model to a correlated two-factor model of psychopathology at three timepoints in a large adolescent community sample (N = 387; 55 % female; 83% Caucasian; M age = 12.1 at wave 1) using self- and parent-reports. Each model was tested within each time-point with 25–28 validators. The bifactor models demonstrated better fit to the data. Child report had stronger invariance across time. Parent report had stronger reliability over time. Cross-informant correlations between the factors at each wave indicated that the bifactor model had slightly poorer convergent validity but stronger discriminant validity than the two-factor model. With notable exceptions, this pattern of results replicated across informants and waves. The overlap between internalizing and externalizing pathology is systematically and, sometimes, non-linearly related to risk factors and maladaptive outcomes. Strengths and weaknesses to modeling psychopathology as two or three factors and clinical and developmental design implications are discussed.

We have conducted a widefield, wideband, snapshot survey using the Australian SKA Pathfinder (ASKAP) referred to as the Rapid ASKAP Continuum Survey (RACS). RACS covers $\approx 90$% of the sky, with multiple observing epochs in three frequency bands sampling the ASKAP frequency range of 700–1 800 MHz. This paper describes the third major epoch at 1 655.5 MHz, RACS-high, and the subsequent imaging and catalogue data release. The RACS-high observations at 1 655.5 MHz are otherwise similar to the previously released RACS-mid (at 1 367.5 MHz) and were calibrated and imaged with minimal changes. From the 1 493 images covering the sky up to declination $\approx +48^\circ$, we present a catalogue of 2 677 509 radio sources. The catalogue is constructed from images with a median root-mean-square noise of $\approx 195$ $\unicode{x03BC}$Jy PSF$^{-1}$ (point-spread function) and a median angular resolution of $11{\stackrel{\prime\prime}{\raise-0pt\hbox{.}}}8 \times 8{\stackrel{\prime\prime}{\raise-0pt\hbox{.}}}1$. The overall reliability of the catalogue is estimated to be 99.18%, and we find a decrease in reliability as angular resolution improves. We estimate the brightness scale to be accurate to 10%, and the astrometric accuracy to be within $\approx 0{\stackrel{\prime\prime}{\raise-0pt\hbox{.}}}6$ in right ascension and $\approx 0{\stackrel{\prime\prime}{\raise-0pt\hbox{.}}}7$ in declination after correction of a systematic declination-dependent offset. All data products from RACS-high, including calibrated visibility datasets, images from individual observations, full-sensitivity mosaics, and the all-sky catalogue are available at the CSIRO ASKAP Science Data Archive.

We evaluated herbicides for controlling the annual grass ventenata [Ventenata dubia (Leers) Coss.], with particular interest in indaziflam, a preemergence cellulose biosynthesis inhibitor. In 2016, indaziflam was applied postemergence alone and in mixture with glyphosate, imazapic, propoxycarbazone-sodium, or rimsulfuron to an improved pasture in southwestern Montana. A non-sprayed control was included for comparison purposes. Canopy cover of each species was assessed annually for 7 yr; cover was grouped by life-form and longevity, and species richness was calculated. Five years (2021) after treatment, the seedbank was assessed. Our results indicated that treatments including indaziflam reduced V. dubia cover 1 to 3 yr and even up to 6 yr after application, with V. dubia cover being zero or close to zero. However, at 7 yr (2023) after treatment, V. dubia was low across all treatments, including the non-sprayed control. Perennial grasses and forbs and annual forbs were generally unaffected by any treatment and did not increase in cover over the 7 yr, even though V. dubia decreased. Two years after treatment, species richness was lowest in treatments that included indaziflam, but at 7 yr, species richness was similar across all treatments. Indaziflam depleted the monocot and dicot seedbank, with fewer than 5 seedlings of any species emerging from treatments that included indaziflam, while other treatments resulted in 60 to 165 seedlings per sample (40 cm3 of soil). In summary, at our study site, a single application of indaziflam controlled V. dubia for 6 yr, appeared to deplete the seedbank at 5 yr, and cover of perennial and annual vegetation and species richness was unaffected. By the end of the study, though, V. dubia cover appeared to be influenced by factors other than herbicide treatments, possibly variable precipitation over time, an exclusion of grazing, and competitive perennial grasses dominating the site.

Highly portable and accessible MRI technology will allow researchers to conduct field-based MRI research in community settings. Previous guidance for researchers working with fixed MRI does not address the novel ethical, legal, and societal issues (ELSI) of portable MRI (pMRI). Our interdisciplinary Working Group (WG) previously identified 15 core ELSI challenges associated with pMRI research and recommended solutions. In this article, we distill those detailed recommendations into a Portable MRI Research ELSI Checklist that offers practical operational guidance for researchers contemplating using this technology.

Objectives: Leveraging the non-monolithic structure of Latin America, which represents a large variability in social determinants of health (SDoH) and high levels of genetic admixture, we aim to evaluate the relative contributions of SDoH and genetic ancestry in predicting dementia risk in Latin American populations

Methods: Community-dwelling participants aged 65 and older (N = 3808) from Cuba, Dominican Republic, Mexico, and Peru completed the 10/66 protocol assessments. Dementia was diagnosed using the cross-culturally validated 10/66 algorithm. The primary outcome measured was the risk of developing dementia. Multivariate linear regression models adjusted for SDoH were used in the main analysis.

Results: We observed extensive three-way (African/European/Native American) genetic ancestry variation between countries. Individuals with higher proportions of Native American (>70%) and African American (>70%) ancestry were more likely to exhibit factors contributing to worse SDoH, such as lower educational levels (p <0.001), lower SES (p < 0.001), and higher frequency of vascular risk factors (p < 0.001). In unadjusted analysis, American individuals with predominant African ancestry exhibited a higher dementia frequency (p = 0.03) and both Native and African ancestry predominant groups showed lower cognitive performance relative to those with higher European ancestry (p < 0.001). However, after adjusting for measures of SDoH, there was no association between ancestry proportion and dementia probability, and ancestry proportions no longer significantly accounted for the variance in cognitive performance (African predominant p = 0.31 [–0.19, 0.59] and Native predominant p = 0.74 [–0.24, 0.33]).

Conclusions: The findings suggest that social and environmental factors play a more crucial role than genetic ancestry in predicting dementia risk in Latin American populations. This underscores the need for public health strategies and policies that address these social determinants to reduce dementia risk in these communities effectively.

Objectives: Because of the continued transition to older populations, various strategies have been developed to estimate the social impact and burden of health care. Regarding mental health, a strategy in the elderly is the measurement of neuropsychiatric symptoms (NPS), these include a wide range of behavioral and psychological manifestations. These are more frequent in the presence of some diseases, such as neurodegenerative syndromes, among which dementias and Parkinson’s disease (PD) stand out. The present study seeks to analyze the frequency of NPS, its relationship with the presence or absence of neurodegenerative syndromes and some characteristics of the elderly and caregivers.

Methods: This is an analysis of data from 12,865 elderly people evaluated within the protocols of the Dementia Research Group 10/66 in 6 Latin American countries (Cuba, Dominican Republic, Puerto Rico, Mexico, Venezuela and Peru). The presence or absence of parkinsonism, dementia and parkinsonism plus dementia (PDD) was identified through previously validated and published Methods. The NPS were assessed using the 12-symptom questionnaire version of the Neuropsychiatric Inventory. Other characteristics such as age, sex and education, in patients and caregivers; socioeconomic status, disability and comorbidities in the elderly; relationship with the elderly, needs and care-burden were assessed in careers.

Results: The most frequent symptoms were depression and sleep disorders in the four groups (without non-NDS neurodegenerative syndromes, parkinsonism, dementia and PDD, ranging from 23% to 49%. About a third of the elderly with parkinsonism, half of those with dementia, and 3 out of 5 of the elderly with PDD had 3 or more NPS. The odds ratios (OR) of each NPS measure by multivariate logistic regression models shown OR from 1.4 to 1.9 in the presence of parkinsonism; between 1.7 and 9.3 in the presence of dementia; and between 1.9 and 10.2 in the presence of PDD.

Conclusions: From a clinical and public mental health perspective, it is necessary to implement systematic Methods for NPS screening, as well as develop support strategies for families and caregivers, mainly of those with neurodegenerative syndromes.

We all consume the humanities through our engagement with the cultural, creative, and historical materials that influence our views on ourselves, others, and the world around us. However, can consumers also be considered humanists? We argue the answer is yes when consumption choices become symbols and expressions of one’s authentic self and meaningful connective points to others. Using hard-core surfing enthusiasts and thrifters as examples, we introduce the notion of fringe consumption as a form of cultural entrepreneurship and public expression of the humanities that centers individuality, authenticity, and otherness in an otherwise dominant mainstream environment that pushes people to always want more of the same.

A surveillance system for measuring patient-level antimicrobial adverse drug events (ADE) may support stewardship activities, however, design and implementation questions remain. In this national survey, stewardship experts favored simple, laboratory-based ADE definitions although there were tensions between feasibility, ability to identify attribution without chart review, and importance of specific ADE.

Tuberculosis infection (TBI) has been associated with increased cardiovascular risks. We aimed to characterize abnormal blood pressure (BP) readings in individuals with TBI. We conducted a retrospective study of adults with TBI presenting for their initial medical visit at a large midwestern U.S. public health clinic between 2019 and 2020. Abnormal BP was defined as having a systolic BP ≥ 130 mmHg and/or a diastolic BP ≥ 80 mmHg. Of 310 individuals with TBI, median age was 36 years (interquartile range 27–48), 34% were male, 64% non-US-born; 58 (18.7%) were previously diagnosed with hypertension. The prevalence of any hypertension (i.e., had a history of hypertension and/or an abnormal BP reading) was 64.2% (95% confidence interval 58.7–69.4). Any hypertension was independently associated with older age, male sex, higher body mass index, and individuals of Black race. In conclusion, any hypertension was present in over half of the adults evaluated for TBI in our clinic. Established hypertension risk factors were also common among this group, suggesting that individuals with TBI could benefit from clinical and public health interventions aiming to reduce the risk of future cardiovascular events.

We present deep near-infrared $K_\textrm{s}$-band imaging for 35 of the 53 sources from the high-redshift ($z \gt 2$) radio galaxy candidate sample defined in Broderick et al. (2022, PASA, 39, e061). These images were obtained using the High-Acuity Widefield K-band Imager (HAWK-I) on the Very Large Telescope. Host galaxies are detected for 27 of the sources, with $K_\textrm{s} \approx 21.6$–23.0 mag (2$^{\prime\prime}$ diameter apertures; AB). The remaining eight targets are not detected to a median $3\unicode{x03C3}$ depth of $K_\textrm{s} \approx 23.3$ mag (2$^{\prime\prime}$ diameter apertures). We examine the radio and near-infrared flux densities of the 35 sources, comparing them to the known $z \gt 3$ powerful radio galaxies with 500-MHz radio luminosities $L_{500\,\textrm{MHz}} \gt 10^{27}$ W Hz$^{-1}$. By plotting 150-MHz flux density versus $K_\textrm{s}$-band flux density, we find that, similar to the sources from the literature, these new targets have large radio to near-infrared flux density ratios, but extending the distribution to fainter flux densities. Five of the eight HAWK-I deep non-detections have a median $3\unicode{x03C3}$ lower limit of $K_\textrm{s} \gtrsim 23.8$ mag (1$.\!^{\prime\prime}$5 diameter apertures); these five targets, along with a further source from Broderick et al. (2022, PASA, 39, e061) with a deep non-detection ($K_\textrm{s} \gtrsim 23.7$ mag; $3\unicode{x03C3}$; 2$^{\prime\prime}$ diameter aperture) in the Southern H-ATLAS Regions $K_\textrm{s}$-band Survey, are considered candidates to be ultra-high-redshift ($z \gt 5$) radio galaxies. The extreme radio to near-infrared flux density ratios ($\gt 10^5$) for these six sources are comparable to TN J0924$-$2201, GLEAM J0856$+$0223 and TGSS J1530$+$1049, the three known powerful radio galaxies at $z \gt 5$. For a selection of galaxy templates with different stellar masses, we show that $z \gtrsim 4.2$ is a plausible scenario for our ultra-high-redshift candidates if the stellar mass $M_\textrm{*} \gtrsim 10^{10.5}$ M$_\odot$. In general, the 35 targets studied have properties consistent with the previously known class of infrared-faint radio sources. We also discuss the prospects for finding more UHzRG candidates from wide and deep near-infrared surveys.

Accelerating COVID-19 Treatment Interventions and Vaccines (ACTIV) was initiated by the US government to rapidly develop and test vaccines and therapeutics against COVID-19 in 2020. The ACTIV Therapeutics-Clinical Working Group selected ACTIV trial teams and clinical networks to expeditiously develop and launch master protocols based on therapeutic targets and patient populations. The suite of clinical trials was designed to collectively inform therapeutic care for COVID-19 outpatient, inpatient, and intensive care populations globally. In this report, we highlight challenges, strategies, and solutions around clinical protocol development and regulatory approval to document our experience and propose plans for future similar healthcare emergencies.

Despite governors’ crucial roles in shaping important policies, including abortion, education, and infrastructure, forecasters have paid little attention to gubernatorial elections. We posit that institutional idiosyncrasies and lack of public opinion data have exacerbated the classic problem facing all election forecasts: there are too many predictors and too few cases, leading to overfitting. To address these problems, we combine new governor and state-level presidential approval data with a machine-learning approach, LASSO, for variable selection. LASSO examines numerous variables but retains only those that substantively improve model performance. Results demonstrate the efficacy of gubernatorial and presidential approval ratings measured two quarters preelection in predicting both incumbent-party vote share and election winners in out-of-sample predictions. For 2022, our approach outperformed the Cook Political Report’s Partisan Voting Index and compared well with 538’s Election Day prediction. For 2024, our LASSO-Popularity model predictions indicate that it will likely be a difficult year for Democrats in gubernatorial contests.