Cost-Effectiveness of Temporary Financial Assistance for Veterans Experiencing Housing Instability.

Importance: The US Department of Veterans Affairs (VA) partners with community organizations (grantees) across the US to provide temporary financial assistance (TFA) to vulnerable veterans through the Supportive Services for Veteran Families (SSVF) program. The goal of TFA for housing-related expenses is to prevent homelessness or to quickly house those who have become homeless.

Objective: To assess the cost-effectiveness of the SSVF program with TFA vs without TFA as an intervention for veterans who are experiencing housing insecurity.

Neurodegenerative biomarkers in different chambers of the eye relative to plasma: an agreement validation study.

Background: Protein biomarkers have been broadly investigated in cerebrospinal fluid and blood for the detection of neurodegenerative diseases, yet a clinically useful diagnostic test to detect early, pre-symptomatic Alzheimer's disease (AD) remains elusive. We conducted this study to quantify Aβ40, Aβ42, total Tau (t-Tau), hyperphosphorylated Tau (ptau181), glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) in eye fluids relative to blood.

Methods: In this cross-sectional study we collected vitreous humor, aqueous humor, tear fluid and plasma in patients undergoing surgery for eye disease.

Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP).

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates.

Research Design And Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%).

Results: T1D characteristics increased progressively with higher genetic risk (P < 0.

For whom are treatments for criminal recidivism effective? Moderator effects from a randomized controlled trial of justice-involved veterans.

Objective: In a recent trial, moral reconation therapy (MRT)-a cognitive-behavioral intervention for criminal recidivism-was not more effective than usual care (UC) for veterans in behavioral health treatment. To determine for whom treatments of recidivism are most effective, we tested if recency of criminal history or psychopathic traits moderated MRT's effects on outcomes.

Method: In a multisite trial, 341 veterans (95.

American Football Play and Parkinson Disease Among Men.

Importance: Parkinsonism and Parkinson disease (PD) are known to result from repetitive head impacts from boxing. Repetitive head impacts from American football may also be associated with increased risk of neurodegenerative pathologies that cause parkinsonism, yet in vivo research on the association between football play and PD is scarce and limited by small samples and equivocal findings.

Objective: To evaluate the association between football participation and self-reported parkinsonism or PD diagnosis.

Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing.

Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models learn. Fill-in-the-blanks problem (e.g.

Vitreous Humor Biomarkers Reflect Pathological Changes in the Brain for Alzheimer's Disease and Chronic Traumatic Encephalopathy.

Background: Patients with eye disease have an increased risk for developing neurodegenerative disease. Neurodegenerative proteins can be measured in the eye; however, correlations between biomarker levels in eye fluid and neuropathological diagnoses have not been established.

Objective: This exploratory, retrospective study examined vitreous humor from 41 postmortem eyes and brain tissue with neuropathological diagnoses of Alzheimer's disease (AD, n = 7), chronic traumatic encephalopathy (CTE, n = 15), both AD + CTE (n = 10), and without significant neuropathology (controls, n = 9).

Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context.

Language Models (LMs) have performed well on biomedical natural language processing applications. In this study, we conducted some experiments to use prompt methods to extract knowledge from LMs as new knowledge Bases (LMs as KBs). However, prompting can only be used as a low bound for knowledge extraction, and perform particularly poorly on biomedical domain KBs.

Evaluating the efficacy of NoteAid on EHR note comprehension among US Veterans through Amazon Mechanical Turk.

Objective: Low health literacy is a concern among US Veterans. In this study, we evaluated NoteAid, a system that provides lay definitions to medical jargon terms in EHR notes to help Veterans comprehend EHR notes. We expected that low initial scores for Veterans would be improved by using NoteAid.

Challenges, Facilitators, and Recommendations for Implementation of Home Dialysis in the Veterans Health Administration: Patient, Caregiver, and Clinician Perceptions.

Background: Home dialysis confers similar survival and greater quality of life than in-center hemodialysis for adults with ESKD but remains underutilized. We examined challenges and facilitators to implementation of home dialysis and identified stakeholder-centered strategies for improving it.

Methods: We conducted a qualitative, cross-sectional, multisite evaluation that included five geographically dispersed Veterans Health Administration (VHA) home dialysis programs.

Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study.

Background: Accurate detection of bleeding events from electronic health records (EHRs) is crucial for identifying and characterizing different common and serious medical problems. To extract such information from EHRs, it is essential to identify the relations between bleeding events and related clinical entities (eg, bleeding anatomic sites and lab tests). With the advent of natural language processing (NLP) and deep learning (DL)-based techniques, many studies have focused on their applicability for various clinical applications.

Evaluating the Effectiveness of NoteAid in a Community Hospital Setting: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Patients.

Background: Interventions to define medical jargon have been shown to improve electronic health record (EHR) note comprehension among crowdsourced participants on Amazon Mechanical Turk (AMT). However, AMT participants may not be representative of the general population or patients who are most at-risk for low health literacy.

Objective: In this work, we assessed the efficacy of an intervention (NoteAid) for EHR note comprehension among participants in a community hospital setting.

Bleeding Entity Recognition in Electronic Health Records: A Comprehensive Analysis of End-to-End Systems.

A bleeding event is a common adverse drug reaction amongst patients on anticoagulation and factors critically into a clinician's decision to prescribe or continue anticoagulation for atrial fibrillation. However, bleeding events are not uniformly captured in the administrative data of electronic health records (EHR). As manual review is prohibitively expensive, we investigate the effectiveness of various natural language processing (NLP) methods for automatic extraction of bleeding events.

Effects of intensive versus standard blood pressure control on domain-specific cognitive function: a substudy of the SPRINT randomised controlled trial.

Background: Results from the Systolic Blood Pressure Intervention Trial (SPRINT) showed that intensive control of systolic blood pressure significantly reduced the occurrence of mild cognitive impairment, but not probable dementia. We investigated the effects of intensive lowering of systolic blood pressure on specific cognitive functions in a preplanned substudy of participants from SPRINT.

Methods: SPRINT was an open-label, multicentre, randomised controlled trial undertaken at 102 sites, including academic medical centres, Veterans Affairs medical centres, hospitals, and independent clinics, in the USA and Puerto Rico.

Neurofilament light chain in the vitreous humor of the eye.

Background: Neurofilament light chain (NfL) is a promising biomarker of neurodegeneration in the cerebrospinal fluid and blood. This study investigated the presence of NfL in the vitreous humor and its associations with amyloid beta, tau, inflammatory cytokines and vascular proteins, apolipoprotein E (APOE) genotypes, Mini-Mental State Examination (MMSE) scores, systemic disease, and ophthalmic diseases.

Methods: This is a single-site, prospective, cross-sectional cohort study.

Using Electronic Data Collection Platforms to Assess Complementary and Integrative Health Patient-Reported Outcomes: Feasibility Project.

Background: The Veteran Administration (VA) Office of Patient-Centered Care and Cultural Transformation is invested in improving veteran health through a whole-person approach while taking advantage of the electronic resources suite available through the VA. Currently, there is no standardized process to collect and integrate electronic patient-reported outcomes (ePROs) of complementary and integrative health (CIH) into clinical care using a web-based survey platform. This quality improvement project enrolled veterans attending CIH appointments within a VA facility and used web-based technologies to collect ePROs.

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.

Background: Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications).

Objective: This study aimed to model such relations and build predictive models using the EHR data from intensive care units.

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network.

Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents.

Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation.

Background: Since its inception, artificial intelligence has aimed to use computers to help make clinical diagnoses. Evidence-based medical reasoning is important for patient care. Inferring clinical diagnoses is a crucial step during the patient encounter.

MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation.

Neural machine translation (NMT) models have achieved state-of-the-art translation quality with a large quantity of parallel corpora available. However, their performance suffers significantly when it comes to domain-specific translations, in which training data are usually scarce. In this paper, we present a novel NMT model with a new word embedding transition technique for fast domain adaption.

BACE1 partial deletion induces synaptic plasticity deficit in adult mice.

BACE1 is the first enzyme involved in APP processing, thus it is a strong therapeutic target candidate for Alzheimer's disease. The observation of deleterious phenotypes in BACE1 Knock-out (KO) mouse models (germline and conditional) raised some concerns on the safety and tolerability of BACE1 inhibition. Here, we have employed a tamoxifen inducible BACE1 conditional Knock-out (cKO) mouse model to achieve a controlled partial depletion of BACE1 in adult mice.

Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study.

Background: The bidirectional encoder representations from transformers (BERT) model has achieved great success in many natural language processing (NLP) tasks, such as named entity recognition and question answering. However, little prior work has explored this model to be used for an important task in the biomedical and clinical domains, namely entity normalization.

Objective: We aim to investigate the effectiveness of BERT-based models for biomedical or clinical entity normalization.

Association of White Matter Rarefaction, Arteriolosclerosis, and Tau With Dementia in Chronic Traumatic Encephalopathy.

Importance: Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with repetitive head impacts, including those from US football, that presents with cognitive and neuropsychiatric disturbances that can progress to dementia. Pathways to dementia in CTE are unclear and likely involve tau and nontau pathologic conditions.

Objective: To investigate the association of white matter rarefaction and cerebrovascular disease with dementia in deceased men older than 40 years who played football and had CTE.

Dissemination and Effectiveness of the Peer Marketing and Messaging of a Web-Assisted Tobacco Intervention: Protocol for a Hybrid Effectiveness Trial.

Background: Smoking continues to be the leading preventable cause of death. Digital Interventions for Smoking Cessation (DISCs) are health communication programs accessible via the internet and smartphones and allow for greater reach and effectiveness of tobacco cessation programs. DISCs have led to increased 6-month cessation rates while also reaching vulnerable populations.