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210 results for “Medical Data”
COREQ checklist: Focus group for 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in medical Data Warehouses'
<p>Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.</p>
Raw Data for the article: Post-crisis debriefing: A tool for improving quality in the medical emergency team system
<p><strong>Objectives: </strong>To examine clinicians' perception of quality of technical and non-technical response to emergencies and application of post crisis debriefing.</p> <p><strong>Design: </strong>Descriptive, anonymous, self-reporting survey on the needs and perception of a post-crisis debriefing implementation.</p> <p><strong>Setting: </strong>Multi-specialist medical institute in Italy focused on solid organ transplantation and organ failure support.</p> <p><strong>Main outcomes: </strong>Perception of application of guidelines and evaluation of debriefing implementation during in-hospital emergencies.</p> <p><strong>Results: </strong>Response rate to the survey was 25% (148 health care workers). Of all respondents, 86% were employed >10 years, 75% were involved in ≤5 emergencies over the previous year. Resuscitation guidelines were considered fully applied by 55%; 64% of respondents considered the teaching programme as sufficient. Of all participants, 97% were aware of the importance of teamwork dynamics, 79% were aware of the importance of the personal performance, and 52% considered emergencies as valid opportunities for professional growth. Leadership was considered important by 45% of respondents; debriefing implemented by 41%, and considered a potentially useful tool by 85%.</p> <p><strong>Conclusion: </strong>Post-crisis debriefing is a way to diffuse self-reflective and life-long learning culture; it is perceived as a powerful tool for improving quality of the rapid response system by the vast majority of those surveyed.</p> <p><strong>Keywords: </strong>Communication; Crisis resource management; Emergency; ICU; Intensive care medicine; Leadership; Patient safety; Teamwork.</p>
Data from: Implementation of a pediatric telemedicine and medication delivery service in a resource-limited setting: A pilot study for clinical safety and feasibility
<p>Objective: Determine the clinical safety and feasibility of implementing a telemedicine and medication delivery service (TMDS) to address gaps in nighttime healthcare access for children in low-resource settings.</p> <p>Results: A total of 391 cases were enrolled from September 9th, 2019 to January 19th, 2021; 89% (347) received a household visit. Most cases were triaged as mild or moderate (92%; 361). Among the severe cases, 83% (20) sought subsequent referred care. The most common complaint was a respiratory problem (63%; 246). At 10-days, 95% (329) of parents reported their child's condition as "improved" or "recovered". Ninety-nine percent (344) rated the TMDS as "good" or "great". The median phone consultation was 20 minutes, time to arrival at the household was 73 minutes and total workflow per case was 114 minutes.</p> <p>Conclusion: The TMDS was a feasible healthcare delivery model with high rates of improved clinical status at 10-days.</p>
Interaction event data logged by the SMASH medication safety dashboard
<p>Interaction event data logged by the SMASH medication safety dashboard from 21 January 2016 to 13 October 2016.</p>
Data for: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education
<p>This is the raw data for the manuscript: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education. The abstract for the study is as follows: </p> <p>Introduction: Many medical education journals use Twitter to garner attention for their articles. The purpose of this study was to test the effects of tweeting on article page views and downloads.</p> <p>Methods: The authors conducted a randomized trial using <em>Academic Medicine</em> articles published in 2015. Beginning in February through May 2018, one article per day was randomly assigned to a Twitter (case) or control group. Daily, an individual tweet was generated for each article in the Twitter group that included the title, #MedEd, and a link to the article. The link delivered users to the article’s landing page, which included immediate access to the HTML full text and a PDF link. The authors extracted HTML page views and PDF downloads from the publisher. To assess differences in page views and downloads between cases and controls, a time-centered approach was used, with outcomes measured at 1, 7, and 30 days.</p> <p>Results: In total, 189 articles (94 cases, 95 controls) were analyzed. After days 1 and 7, there were no statistically significant differences between cases and controls on any metric. On day 30, HTML page views exhibited a 63% increase for cases (M=14.72, SD=63.68) when compared to controls (M=9.01, SD=14.34; incident rate ratio=1.63, p=0.01). There were no differences between cases and controls for PDF downloads on day 30.</p> <p>Discussion: Contrary to the authors’ hypothesis, only one statistically significant difference in page views between the Twitter and control groups was found. These findings provide preliminary evidence that after 30 days a tweet can have a small positive effect on article page views.</p>
pGAN Synthetic Dataset: A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
<p>Synthetic dataset for <strong>A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs</strong></p> <p><strong> Dataset specification:</strong></p> <ul> <li>MRI images of Vertebral Units labelled based on region</li> <li>Dataset is comprised of 10000 pairs of images and labels</li> <li>Image and label pair number k can be selected by: synthetic_dataset['images'][k] and synthetic_dataset['regions'][k]</li> <li>Images are 3D of size (9, 64, 64)</li> <li>Regions are stored as an integer. Mapping is 0: cervical, 1: thoracic, 2: lumbar</li> </ul> <p>Arxiv paper: <a href="https://arxiv.org/abs/2106.13199">https://arxiv.org/abs/2106.13199</a><br> Github code: <a href="https://github.com/tcoroller/pGAN/">https://github.com/tcoroller/pGAN/</a></p> <p>Abstract:</p> <p>Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.</p>
A concept for FAIR clinical medication data usage - From care to research with OMOP: literature list of OHDSI studies
<p>This list of papers has been reviewed for the usage of drug data and to answer the question on what drug level the study was done. </p> <p>We checked whether drug ingredient level or drug component with dose and unit was required for the studies. </p>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part I)
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} }</pre> </blockquote>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part II)
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} </pre> </blockquote>
Data for: Gender medicine teaching increases medical students' gender awareness: results of a quantitative online survey
<p><span><strong>Background</strong> </span></p> <p><span>Gender medical knowledge is insufficiently integrated into university teaching in Germany. Gender awareness represents a key competence to integrate this knowledge into one's medical practice. This study is the first survey of the gender awareness of medical students in Germany.</span></p> <p><strong><span>Methods</span> </strong></p> <p><span>From April to July 2021, a quantitative cross-sectional survey in an online format using the 'Nijmegen Gender Awareness in Medicine Scale' (2008) was conducted at four German universities (Charité Berlin, Friedrich-Schiller-University Jena, Ludwig-Maximilians-University Munich, and the University of Cologne) with a varied implementation of teaching gender medicine. Students indicated their agreement or disagreement with assumptions and knowledge about the influence of gender in everyday medical practice (gender sensitivity), as well as gender role stereotypes towards patients and physicians (gender role ideology). </span></p> <p><strong><span>Results </span></strong></p> <p><span>The 750 included participants showed a relatively high gender sensitivity and low gender role stereotyping towards patients and doctors. The curricular implementation of gender medicine of the universities showed to have a significant influence on the students' gender sensitivity, as well as on their gender role stereotyping towards patients. Students who reported having taken classes in gender medicine showed significantly higher level of gender sensitivity. Cis-males showed significantly lower gender sensitivity and significantly higher gender role stereotyping.</span></p> <p><span><strong>Conclusion</strong> </span></p> <p><span>Implementation of gender medicine in the medical curriculum, attending courses on gender education and one's gender have a significant impact on medical students' gender competencies. These results support the need for structural integration of gender in medical education and gender trainings at medical schools in Germany.</span></p>
Deidentified public survey data for: Patterns and perceptions of nicotine use among U.S. adolescents and young adults receiving medication treatment for opioid use disorder
<p>Nicotine use among U.S. youth is cause for concern, as previous studies have shown that nicotine use in adolescence increases the risk of developing substance use disorders later in life. This exploratory study aimed to understand patterns of nicotine use and perceptions of various nicotine products among adolescents and young adults (AYA) receiving medication treatment for opioid use disorder (MOUD). We administered an adapted version of the National Youth Tobacco Survey via REDCap to AYA (n=32) receiving outpatient care in the Medication-Assisted Treatment of Addiction at Nationwide Children's Hospital in Columbus, Ohio, U.S.A. Thirty (97%) participants had tried a combustible cigarette and 27 (90%) had tried an electronic cigarette. By age 13, nineteen (61%) participants had tried combustible cigarettes and eight (25%) had tried opioids. Twenty-two (71%) participants reported smoking combustible cigarettes every day for the past 30 days, and 15 (48%) reported smoking more than 10 cigarettes per day on average. Only ten (32%) participants reported e-cigarette use in the last 30 days. Participants universally agreed that tobacco products are dangerous, and twenty (67%) current tobacco users reported that they planned to quit in the next year. Nicotine use patterns among AYA receiving MOUD differ from that previously shown in the general population, primarily by high prevalence of nicotine use in early adolescence and high current combustible cigarette use. Interventions such as universal screening for nicotine use before age 13 and tailored smoking cessation programs for AYA with OUD may help optimize care for these individuals.</p>
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
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Deidentified public survey data for: Patterns and perceptions of nicotine use among U.S. adolescents and young adults receiving medication treatment for opioid use disorder
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Data for: Gender medicine teaching increases medical students' gender awareness: results of a quantitative online survey
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Data from: Implementation of a pediatric telemedicine and medication delivery service in a resource-limited setting: A pilot study for clinical safety and feasibility
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Data from: Knowledge, provision of information and barriers to high quality medication abortion provision by pharmacists in Uttar Pradesh, India
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Data from: From trial to implementation, bringing team-based learning online – Duke-NUS Medical School's response to the COVID-19 pandemic
<p>The restrictions imposed by the COVID-19 pandemic resulted in Duke-NUS Medical School moving all their lessons online. Duke-NUS employs a team-based learning (TBL) pedagogy, which depends heavily on student discussion. In 2015, our university had implemented an eLearning week where lessons were conducted online. Using the already present online assessment processes, the data, insights and student feedback allowed for swift implementation of an online TBL module for home-based learning in response to the pandemic in 2020. These protocols were modified over the weeks, guided by feedback from students and faculty. An analysis of this online TBL module is presented herein.</p>
Publicly available medical text data with authentic quality
<p>This dataset is the public medical text record (progress notes) written in Japanese.</p> <p>Any researchers can use this dataset without privacy issues. </p> <p>CC BY-NC 4.0</p> <p>crowd.zip: 9,756 pseudo progress notes written by crowd workers</p> <p>crowd_evaluated.zip: 83 pseudo progress notes with authentic quality written by crowd workers</p> <p>MD.zip: 19 pseudo progress notes written by medical doctors</p> <p> </p> <p>Reference:</p> <p>Kagawa, R., Baba, Y., & Tsurushima, H. (2021, December). A practical and universal framework for generating publicly available medical notes of authentic quality via the power of crowds. In <em>2021 IEEE International Conference on Big Data (Big Data)</em> (pp. 3534-3543). IEEE.</p> <p><a href="http://hdl.handle.net/2241/0002002333">http://hdl.handle.net/2241/0002002333</a></p> <p>The supplemental files of the paper are here: <a href="https://github.com/rinabouk/HMData2021">https://github.com/rinabouk/HMData2021</a></p>
Data from: Medical-device recalls in the UK and the device-regulation process: retrospective review of safety notices and alerts
Background: Medical devices are used widely for virtually every disease and condition. Although devices are subject to regulation, the number of recalls, the clinical data requirements for regulation and the impact on patient safety are poorly understood. Methods: The authors defined a device using European directives and used publicly available information on the Medicines and Health Regulatory Authority website to determine the number of devices recalled from January 2006 to December 2010. Two reviewers independently assessed Field Safety Notices and Medical Device Alerts. The authors wrote to manufacturers to obtain further information and clinical data, and summarised data by year, Conformité Européenne classification, indication, and Food and Drug Administration recall system of severity. Results: In total, 2124 field safety notices were issued over the 5-year period, an increase of 1220% (62 in 2006 to 757 in 2010). 447 Medical Device Alerts were issued in the same period, and 44% were assessed as a reasonable probability of causing serious adverse health consequences or death. The authors wrote to 192 manufacturers of withdrawn devices and received 101 (53%) replies; only four (2.1%) provided the clinical data the authors requested. A lack of available transparent data prevented full analyses of the safety impact. Of the highest-risk recalled devices, more than half were related to the cardiovascular system (25%) or musculoskeletal system (33%), and 88% (95% CI 80% to 97%) were assessed as a reasonable probability of causing serious adverse health consequences or death. For low-risk devices, the figure was 34% (95% CI 26% to 42%). Conclusion: The number of medical devices subject to recalls or warnings in the UK has risen dramatically. A substantial number of these devices may have caused serious adverse effects in patients and contributed to healthcare costs. Significant problems exist in the UK with a lack of access to transparent data and a registry of the highest-risk devices.
Data from: What influence do courses at medical school and personal experience have on interest in practicing family medicine? – results of a student survey in Hessia
Aim: Against the background of an impending shortage of family practitioners, it is important to investigate the factors influencing the choice to become one. The aim of this study was to identify factors that encourage medical students to choose to practice family medicine. Method: Using a questionnaire, students in the fourth and fifth years of their studies in the Federal State of Hesse were asked about the factors that had influenced their choice of medical specialty and their experience of courses in family medicine. Predictors of an interest in practicing family medicine were calculated using multiple logistic regression. Results: 361 questionnaires were returned, representing a response rate of 70.9%. Confirmation of personal strengths, an interest in the field, and practical experience of the subject generally turned out to be important factors influencing the choice of medical specialty. 49.3% of students expressed an interest in practicing family medicine. A link existed between an interest in working as a family doctor and the opportunity to take over an existing practice, experience of medicine in rural areas, and an appreciation of the conditions of work. With regard to education at medical school, positive experiences during a clinical traineeship in family medicine and positive role models among teachers of general practice were identified as predictors. Conclusion: Almost half the medical students were open to the idea of practicing family medicine. Experience of medicine in rural areas and positive experiences of courses in general practice were linked to an increased interest in working as a family doctor. To promote this interest, it may be a promising approach to increase opportunities to collect experience of medicine in rural areas, and to encourage highly motivated teaching practices.
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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)
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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.