This dataset provides information on 2,629 in United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 1,553 in North Carolina, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Background: The Training in Research for Academic Neurologists to Sustain Careers and Enhance the Numbers of Diverse Scholars (TRANSCENDS) program is a career advancement opportunity for individuals underrepresented in biomedical research, funded by the National Institute and Neurological Disorders and Stroke; and American Academy of Neurology (AAN).

Objective: To report on qualitative and quantitative outcomes in TRANSCENDS.

Design: Early career individuals (neurology fellows and junior faculty) from groups underrepresented in medicine were competitively selected from a national pool of applicants (2016-2019). TRANSCENDS activities comprised an online Clinical Research degree program, monthly webinars, AAN meeting activities, and mentoring. Participants were surveyed during and after completion of TRANSCENDS to evaluate program components.

Outcomes: Of 23 accepted scholars (comprising four successive cohorts), 56% were women; 61% Hispanic/Latinx, 30% Black/African American, 30% assistant professors. To date, 48% have graduated the TRANSCENDS program and participants have published 180 peer-reviewed articles. Mentees' feedback noted that professional skills development (i.e., manuscript and grant writing), networking opportunities, and mentoring were the most beneficial elements of the program. Stated opportunities for improvement included: incorporating a mentor-the-mentor workshop, providing more transitional support for mentees in the next stage of their careers, and requiring mentees to provide quarterly reports.

Conclusions: TRANSCENDS is a feasible program for supporting underrepresented in medicine neurologists towards careers in research and faculty academic appointments attained thus far have been sustained. While longer term outcomes and process enhancements are warranted, programs like this may help increase the numbers of diverse academic neurologists, and further drive neurological innovation.

Objective: To examine age and sex differences in burnout, career satisfaction, and well-being in US neurologists. Methods: Quantitative and qualitative analyses of men’s (n = 1,091) and women’s (n = 580) responses to a 2016 survey of US neurologists. Results: Emotional exhaustion in neurologists initially increased with age, then started to decrease as neurologists got older. Depersonalization decreased as neurologists got older. Fatigue and overall quality of life in neurologists initially worsened with age, then started to improve as neurologists got older. More women (64.6%) than men (57.8%) met burnout criteria on univariate analysis. Women respondents were younger and more likely to work in academic and employed positions. Sex was not an independent predictive factor of burnout, fatigue, or overall quality of life after controlling for age. In both men and women, greater autonomy, meaning in work, reasonable amount of clerical tasks, and having effective support staff were associated with lower burnout risk. More hours worked, more nights on call, higher outpatient volume, and higher percent of time in clinical practice were associated with higher burnout risk. For women, greater number of weekends doing hospital rounds was associated with higher burnout risk. Women neurologists made proportionately more negative comments than men regarding workload, work–life balance, leadership and deterioration of professionalism, and demands of productivity eroding the academic mission. Conclusions: We identified differences in burnout, career satisfaction, and well-being in neurologists by age and sex. This may aid in developing strategies to prevent and mitigate burnout and promote professional fulfillment for different demographic subgroups of neurologists.

This dataset provides information on 934 in Missouri, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 2,048 in Ohio, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 834 in Connecticut, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 1,162 in Arizona, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Objectives: To evaluate neurologists and other clinicians’ contributions to U.S. ALS diagnostic timelines. Background: Over the past two decades, the average time to ALS diagnosis in the U.S. has remained unchanged at 12 months. ALS patients see 3-4 clinicians prior to referral to an ALS specialist for diagnosis confirmation and/or treatment initiation. There is an urgent need to identify where delays occur, so that targeted clinician awareness may be raised about early suspicion and referrals. Methods: Review of Medicare claims database for health care utilization patterns by ALS beneficiaries during diagnostic journey. Survey of typical clinic wait times for new consultations reported by 75-78 ALS Certified Treatment Centers of Excellence (2019-2021). Results: During 2011-2021, 78,520 Medicare beneficiaries were diagnosed with ALS (T0). The mean (median) timelines between first neurologist ambulatory visit and T0, is 16.5 (11.0) months; mean ± SD for ALS/neuromuscular providers being 9.6 ± 12.6 months versus 16.7 ± 17.5 months for non-neuromuscular neurologists. During the 12-months preceding T0, an ALS patient undergoes median(max) 1.5(4.0) brain-MRIs, 1.6(6.0) spine-MRIs, and 1.3(4.0) electromyography studies. Greater than 75% of ALS centers consistently report ≤ 4 week wait times for new ALS consults. This study introduces “thinkALS,” an easy-to-use clinical diagnostic and referral guide for non-ALS neurologists to tackle this challenge. Conclusions: This study is the first to provide metrics on how non-neuromuscular/ALS specialists contribute to ALS diagnostic timelines in the U.S.

Parkinson’s disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson’s disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson’s disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson’s disease. In addition, the features attributed to the mild cognitive impairment in Parkinson’s disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson’s disease.

This dataset provides information on 76 in Massachusetts, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Components of the proposed CNN model applied on the VMD data.