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733
datasets available to search
ShareScore release 0.9.0
Dataset results
733 results for “physicians”
Effect of the Early Ultrasound-guided Femoral Nerve Block Performed by Emergency Physicians on Pre-operative Opioids Usage in Patients With Proximal Femoral Fractures
ClinicalTrials.gov study NCT05515718. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Evaluation of Electronic Portal Messaging and Embedded Asynchronous Care on Physician-Assisted Smoking Quit Attempts
ClinicalTrials.gov study NCT05172219. IPD Sharing: NO. Countries: 1. Publications: 1.
Study to Assess the Burden of Mild Outcomes (Physician Office Visits) Due to Influenza in the United States
ClinicalTrials.gov study NCT02019732. IPD Sharing: Not stated. Countries: 1. Publications: 1.
SimCare: Physician Intervention to Improve Diabetes Care
ClinicalTrials.gov study NCT00262704. IPD Sharing: Not stated. Countries: 1. Publications: 7.
The Impact of Combined Mindfulness-Based Interventions and Nutritional Counseling on Physician Burnout
ClinicalTrials.gov study NCT04336436. IPD Sharing: NO. Countries: 1. Publications: 5.
TElemedicine to Replace Face-to-face Physician Consultation in Patients With HyperTension : a Pilot Randomized-controlled Trial
ClinicalTrials.gov study NCT04542564. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Unwinding Physician Anxiety
ClinicalTrials.gov study NCT04137081. IPD Sharing: NO. Countries: 1. Publications: 1.
Study of Sacituzumab Govitecan-hziy Versus Treatment of Physician's Choice in Patients With Previously Untreated Locally Advanced Inoperable or Metastatic Triple-Negative Breast Cancer
ClinicalTrials.gov study NCT05382299. IPD Sharing: NO. Countries: 30. Publications: 1.
Data from: Qualitative study of physicians' varied uses of biomedical research in the USA
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Data from: What keeps family physicians busy in Portugal? A multi-centre observational study of work other than direct patient contacts
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Data from: Shared decision-making as a cost-containment strategy: US physician reactions from a cross-sectional survey
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Data from: Understanding the role of physician attire on patient perceptions: a systematic review of the literature— targeting attire to improve likelihood of rapport (TAILOR) investigators
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Data from: Shaping medicinal product information – a before and after study exploring physicians’ perspectives on the summary of product characteristics
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Medical data formatting to improve physician interpretation speed in the military healthcare system
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Data from: Physician champions’ perspectives and practices on electronic health records implementation: challenges and strategies
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Data from: Gender disparity in physician authorship among commentary articles in high impact medical journals
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Does autotext usage decrease documentation time among resident physicians? A retrospective analysis of EHR usage data
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Datasets for Tweets from Anonymous Physicians about COVID-19 in the U.S.
<p>This dataset was created for a project that assessed Twitter data from physicians posted anonymously by administrators of a specific Twitter user page to better understand physician perspectives and sentiments about COVID-19 in the United States. </p> <p>Tweet identifiers are contained in the 'tweet_identifiers.csv file'</p> <p>Other files contain sentiment analysis data; one file used vaderSentiment in Python 3, and the other file used NRC in R (see sources below for further information and use of these packages.</p> <ol> <li>Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</li> <li>NRC Emotion Lexicon, Saif M. Mohammad and Peter D. Turney, NRC Technical Report, December 2013, Ottawa, Canada.</li> <li>Jockers ML (2015). <em>Syuzhet: Extract Sentiment and Plot Arcs from Text</em>. <a href="https://github.com/mjockers/syuzhet">https://github.com/mjockers/syuzhet</a>.</li> </ol> <p>Code used specifically for this project may be found at: https://github.com/sullkath/tweet_analysis</p> <p>Link to paper publication: </p> <p>Pre-print in bioRxiv available at: </p>
Data from: A virtual molecular tumor board platform to improve efficiency and scalability of delivering precision oncology to physicians and their patients
OBJECTIVES: Scalable informatics solutions that provide molecularly-tailored treatment recommendations to clinicians are needed to streamline Precision Oncology in care settings. MATERIALS AND METHODS: We developed a cloud-based virtual molecular tumor board (VMTB) platform that included a knowledgebase, scoring model, rules engine, an asynchronous virtual chat room and a reporting tool that generated a treatment plan for each of the 1725 patients based on their molecular profile, previous treatment history, structured trial eligibility criteria, clinically relevant cancer gene-variant assertions, biomarker-treatment associations, and current treatment guidelines. The VMTB systematically allows clinician users to combine expert-curated data and structured data from clinical charts along with molecular testing data to develop consensus on treatments, especially those that require off-label and clinical trial considerations. RESULTS: The VMTB was used as part of the cancer care process for a focused subset of 1725 patients referred by advocacy organizations wherein resultant personalized reports were successfully delivered to treating oncologists. Median turnaround time from data receipt to report delivery decreased from 14 days to 4 days over 4 years while the volume of cases increased nearly twofold each year. Using a novel scoring model for ranking therapy options, oncologists chose to implement the VMTB-derived therapies over others, except when pursuing immunotherapy options without molecular support. DISCUSSION: VMTBs will play an increasingly critical role in precision oncology as the compendium of biomarkers and associated therapy options available to a patient continues to expand. CONCLUSION: Further development of such clinical augmentation tools that systematically combine patient-derived molecular data, real world evidence from electronic health records, and expert curated knowledgebases on biomarkers with computational tools for ranking best treatments can support care pathways at point of care.
Data from: Implementation of electronic charting is not associated with significant change in physician productivity in an academic emergency department
Objectives: To compare physician productivity and billing before and after implementation of electronic charting in an academic ED. Materials and Methods: This retrospective, blinded, observational study compared the 6 months pre-implementation (January to June 2012) with the 6 months post-implementation one year later (January to June 2013). Thirty-one ED physicians were recruited, with each physician acting as his/her own control in a before-after design. Productivity was measured via total number of encounters and "productivity index" defined as worked relative value units divided by the clinical full-time equivalent. Values for charges, encounters and productivity index were determined during each study period and separately for procedures, observational stays, and critical care. Results: No differences were found for total productivity index per month (758 [628,876] pre-group vs. 756 [673,886] post-group; p=NS). There was, however, a 9% decrease in total encounters per month (136 [101,163] pre-group vs. 126 [99,159] post-group; p=0.02). Significant decreases were seen across all observation stay categories. Conversely, significant increases were seen across all critical care categories. There was no difference in total charges per month. Discussion: This is one of few studies to demonstrate minimal disruption in physician productivity after transitioning to electronic documentation. The reasons for these findings are likely multifactorial. Conclusion: In this study, implementation of electronic charting was not associated with decreases in productivity or billing for total ED care, but may be associated with increases for critical care and decreases for observational stays.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.