The Impact of Primary Care Physician Capacity on Preventable Hospitalizations: Identifying Bright Spots in the Appalachian & Mississippi Delta Regions.

Introduction: Several studies have documented that higher rates of primary care physicians are associated with lower rates of preventable hospitalizations. Counties with higher rates of preventable hospitalizations are found in the Appalachian and Mississippi (MS) Delta Regions.

Purpose: (1) To determine if the association of primary care capacity with preventable hospitalizations is different in the Appalachian and MS Delta regions compared to the rest of the U.

Relationship Between Primary Care Physician Capacity and Usual Source of Care.

Background: The NASEM Primary Care Report and Primary Care scorecard highlighted the importance of primary care physician (PCP) capacity and having a usual source of care (USC). However, research has found that PCP capacity and USC do not always correlate. This exploratory study compares geographic patterns and the characteristics of counties with similar rates of PCP capacity but varying rates of USC.

Wearable biofeedback device to assess gait features and improve gait pattern in people with parkinson's disease: a case series.

Introduction: People with Parkinson's Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects' awareness of their gait disturbances.

Assessment of Sensorized Insoles in Balance and Gait in Individuals With Parkinson's Disease.

Individuals with Parkinson's disease (PD) are characterized by gait and balance disorders limiting their independence and quality of life. Home-based rehabilitation programs, combined with drug therapy, demonstrated to be beneficial in the daily-life activities of PD subjects. Sensorized shoes can extract balance- and gait-related data in home-based scenarios and allow clinicians to monitor subjects' activities.

Trunk Control Test as a Main Predictor of the Modified Barthel Index Score at Discharge From Intensive Post-acute Stroke Rehabilitation: Results From a Multicenter Italian Study.

Objectives: To verify whether trunk control test (TCT) upon admission to intensive inpatient post-stroke rehabilitation, combined with other confounding variables, is independently associated with discharge mBI.

Design: Multicentric retrospective observational cohort study.

Setting: Two Italian inpatient rehabilitation units.

Artificial intelligence for the recognition of benign lesions of vocal folds from audio recordings.

Objective: The diagnosis of benign lesions of the vocal fold (BLVF) is still challenging. The analysis of the acoustic signals through the implementation of machine learning models can be a viable solution aimed at offering support for clinical diagnosis.

Materials And Methods: In this study, a support vector machine was trained and cross-validated (10-fold cross-validation) using 138 features extracted from the acoustic signals of 418 patients with polyps, nodules, oedema, and cysts.

Estimation of Spatiotemporal Gait Parameters in Walking on a Photoelectric System: Validation on Healthy Children by Standard Gait Analysis.

The use of stereophotogrammetry systems is challenging when targeting children's gait analysis due to the time required and the need to keep physical markers in place. For this reason, marker-less photoelectric systems appear to be a solution for accurate and fast gait analysis in youth. The aim of this study is to validate a photoelectric system and its configurations (LED filter setting) on healthy children, comparing the kinematic gait parameters with those obtained from a three-dimensional stereophotogrammetry system.

Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors.

Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision.

Emergence and pandemic spread of small ruminant lentiviruses.

Small ruminant lentiviruses (SRLVs) cause chronic, persistent infections in populations of domestic sheep () and goats () worldwide. The vast majority of SRLV infections involve two genotypes (A and B) that spread in association with the emergence of global livestock trade. However, SRLVs have likely been present in Eurasian ruminant populations since at least the early Neolithic period.

Identifying health centers in areas with low COVID-19 vaccination rates & high rates of vaccine hesitancy.

Context: Large numbers of US adults are vaccinated, but COVID-19 vaccine hesitancy remains high. Health centers funded by the Health Resources and Services Administration (HRSA) have played a major role in COVID-19 vaccinations and have the potential to vaccinate even larger numbers of people. Objective: To identify U.

EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review.

Objective: Disorders of consciousness (DoC) are acquired conditions of severely altered consciousness. Electroencephalography (EEG)-derived biomarkers have been studied as clinical predictors of consciousness recovery. Therefore, this study aimed to systematically review the methods, features, and models used to derive prognostic EEG markers in patients with DoC in a rehabilitation setting.

Calling Your Aunt Bertha for Social Assets: Family Medicine and Social Determinants of Health.

Purpose: The sociopolitical determinants of health drive health outcomes and inequities in the United States. Primary care practices are, increasingly, expected by payers and policy makers to assess patients' social needs. Resource referral platforms provide physicians with information and referral systems for community resources.

Design and implementation of a Stroke Rehabilitation Registry for the systematic assessment of processes and outcomes and the development of data-driven prediction models: The STRATEGY study protocol.

Background: Stroke represents the second preventable cause of death after cardiovascular disease and the third global cause of disability. In countries where national registries of the clinical quality of stroke care have been established, the publication and sharing of the collected data have led to an improvement in the quality of care and survival of patients. However, information on rehabilitation processes and outcomes is often lacking, and predictors of functional outcomes remain poorly explored.

Using State All-Payer Claims Data to Identify the Active Primary Care Workforce: A Novel Study in Virginia.

Purpose: Primary care is the foundation of the health care workforce and the only part that extends life and improves health equity. Previous research on the geographic and specialty distribution of physicians has relied on the American Medical Association's Masterfile, but these data have limitations that overestimate the workforce.

Methods: We present a pragmatic, systematic, and more accurate method for identifying primary care physicians using the National Plan and Provider Enumeration System (NPPES) and the Virginia All-Payer Claims Database (VA-APCD).

Cross-Validation of Machine Learning Models for the Functional Outcome Prediction after Post-Stroke Robot-Assisted Rehabilitation.

The state of the art is still lacking an extensive analysis of which clinical characteristics are leading to better outcomes after robot-assisted rehabilitation on post-stroke patients. Prognostic machine learning-based models could promote the identification of predictive factors and be exploited as Clinical Decision Support Systems (CDSS). For this reason, the aim of this work was to set the first steps toward the development of a CDSS, by the development of machine learning models for the functional outcome prediction of post-stroke patients after upper-limb robotic rehabilitation.

Cross-validation of predictive models for functional recovery after post-stroke rehabilitation.

Background: Rehabilitation treatments and services are essential for the recovery of post-stroke patients' functions; however, the increasing number of available therapies and the lack of consensus among outcome measures compromises the possibility to determine an appropriate level of evidence. Machine learning techniques for prognostic applications offer accurate and interpretable predictions, supporting the clinical decision for personalised treatment. The aim of this study is to develop and cross-validate predictive models for the functional prognosis of patients, highlighting the contributions of each predictor.

Intra- and inter-rater reliability of the Italian Fugl-Meyer assessment of upper and lower extremity.

Purpose: To assess the intra- and inter-rater reliability motor and sensory functioning, balance, joint range of motion and joint pain subscales of the Italian Fugl-Meyer Assessment (FMA) Upper Extremity (FMA-UE) and Lower Extremity (FMA-LE) at the item- subtotal- and total-level in patients with sub-acute stroke.

Materials And Methods: The FMA was administered to 60 patients with sub-acute stroke (mean age ± SD = 75.4 ± 10.

(Even Off-Label) Cochlear Implantation in Single-Sided Deafness and Asymmetric Hearing Loss Results in Measurable Objective and Subjective Benefit.

Objective: To quantify objective and subjective outcomes in cochlear implant (CI) recipients with asymmetric hearing loss, including single-sided deafness (SSD) whose candidacy was determined on an ear-specific basis when word recognition was 50% or less.

Study Design: Retrospective case review.

Setting: Tertiary referral center.

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions.

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving.

Effects of control strategies on gait in robot-assisted post-stroke lower limb rehabilitation: a systematic review.

Background: Stroke related motor function deficits affect patients' likelihood of returning to professional activities, limit their participation in society and functionality in daily living. Hence, robot-aided gait rehabilitation needs to be fruitful and effective from a motor learning perspective. For this reason, optimal human-robot interaction strategies are necessary to foster neuroplastic shaping during therapy.

Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.

Background: Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with new trends in personalised medical therapies, is expected to change clinical practice dramatically. The emerging field of Rehabilomics is only possible if methodologies are based on biomedical data collection and analysis.

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction.

Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission.

Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients With Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study.

Background: Due to continuous advances in intensive care technology and neurosurgical procedures, the number of survivors from severe acquired brain injuries (sABIs) has increased considerably, raising several delicate ethical issues. The heterogeneity and complex nature of the neurological damage of sABIs make the detection of predictive factors of a better outcome very challenging. Identifying the profile of those patients with better prospects of recovery will facilitate clinical and family choices and allow to personalize rehabilitation.