5 datasets found
  1. f

    Work-related activities during non-work time.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Mohd Fadhli Mohd Fauzi; Hanizah Mohd Yusoff; Nur Adibah Mat Saruan; Rosnawati Muhamad Robat (2023). Work-related activities during non-work time. [Dataset]. http://doi.org/10.1371/journal.pone.0241577.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Mohd Fadhli Mohd Fauzi; Hanizah Mohd Yusoff; Nur Adibah Mat Saruan; Rosnawati Muhamad Robat
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Work-related activities during non-work time.

  2. Data from: Development and validation of a score to detect paroxysmal atrial...

    • zenodo.org
    • data.niaid.nih.gov
    • +1more
    doc, pdf
    Updated Jul 19, 2024
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    Timo Uphaus; Mark Weber-Krüger; Martin Grond; Gerrit Toenges; Anke Jahn-Eimermacher; Marek Jauss; Paulus Kirchhof; Rolf Wachter; Klaus Gröschel; Timo Uphaus; Mark Weber-Krüger; Martin Grond; Gerrit Toenges; Anke Jahn-Eimermacher; Marek Jauss; Paulus Kirchhof; Rolf Wachter; Klaus Gröschel (2024). Data from: Development and validation of a score to detect paroxysmal atrial fibrillation after stroke [Dataset]. http://doi.org/10.5061/dryad.ms3m3n2
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    pdf, docAvailable download formats
    Dataset updated
    Jul 19, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Timo Uphaus; Mark Weber-Krüger; Martin Grond; Gerrit Toenges; Anke Jahn-Eimermacher; Marek Jauss; Paulus Kirchhof; Rolf Wachter; Klaus Gröschel; Timo Uphaus; Mark Weber-Krüger; Martin Grond; Gerrit Toenges; Anke Jahn-Eimermacher; Marek Jauss; Paulus Kirchhof; Rolf Wachter; Klaus Gröschel
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Objective: Prolonged monitoring times (72h) are recommended to detect paroxysmal atrial fibrillation (pAF) after ischemic stroke, but not yet clinical practice; therefore, an individual patient selection for prolonged ECG monitoring might increase the diagnostic yield of pAF in a resource-saving manner. Methods: We used individual patient data from three prospective studies (ntotal=1556) performing prolonged Holter ECG monitoring (at least 72h) and centralized data evaluation after TIA or stroke in patients with sinus rhythm. Based on the TRIPOD guideline, a clinical score was developed on one cohort, internally validated by bootstrapping and externally validated on two other studies. Results: pAF was detected in 77 (4.9%) of 1556 patients during 72h-Holter monitoring. After logistic regression analysis with variable selection, age and the qualifying stroke event (categorised as stroke severity with NIH-SS≤5 (OR 2.4 vs. TIA; 95%CI0.8-6.9,p=0.112) or stroke with NIH-SS>5 (OR 7.2 vs. TIA; 95%CI 2.4-21.8,p<0.001)) were found to be predictive for the detection of pAF within 72h-Holter monitoring and included in the final score (Age: 0.76 points/year, Stroke Severity NIH-SS≤5 = 9 points, NIH-SS>5 = 21 points; to Find AF, AS5F). The high risk group defined by AS5F is characterized by a predicted risk between 5.2% and 40.8% for detection of pAF with a number needed to screen of 3 for the highest observed AS5F points within the study population. Regarding the low number of outcomes before generalization of AS5F the results need replication. Conclusion: The AS5F score can select patients for prolonged ECG monitoring after ischemic stroke to detect pAF.

  3. The improved six-factor model.

    • plos.figshare.com
    xls
    Updated Jun 10, 2023
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    Mohd Fadhli Mohd Fauzi; Hanizah Mohd Yusoff; Nur Adibah Mat Saruan; Rosnawati Muhamad Robat (2023). The improved six-factor model. [Dataset]. http://doi.org/10.1371/journal.pone.0241577.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Mohd Fadhli Mohd Fauzi; Hanizah Mohd Yusoff; Nur Adibah Mat Saruan; Rosnawati Muhamad Robat
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The improved six-factor model.

  4. f

    Initial scale with 23 items.

    • plos.figshare.com
    • figshare.com
    xls
    Updated May 31, 2023
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    Mohd Fadhli Mohd Fauzi; Hanizah Mohd Yusoff; Nur Adibah Mat Saruan; Rosnawati Muhamad Robat (2023). Initial scale with 23 items. [Dataset]. http://doi.org/10.1371/journal.pone.0241577.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Mohd Fadhli Mohd Fauzi; Hanizah Mohd Yusoff; Nur Adibah Mat Saruan; Rosnawati Muhamad Robat
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Initial scale with 23 items.

  5. n

    Test dataset for: "Automated diagnosis of atrial fibrillation in 24-hour...

    • narcis.nl
    • data.mendeley.com
    Updated Sep 23, 2021
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    Zhang, P (via Mendeley Data) (2021). Test dataset for: "Automated diagnosis of atrial fibrillation in 24-hour Holter recording based on deep learning:a study with randomized and real-world data validation" [Dataset]. http://doi.org/10.17632/44htzjcgsz.3
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    Dataset updated
    Sep 23, 2021
    Dataset provided by
    Data Archiving and Networked Services (DANS)
    Authors
    Zhang, P (via Mendeley Data)
    Description

    This test dataset comprised of 800 24-hour Holter recordings from the test set of the randomized clinical cohort in the paper. Each recording includes the RR-interval data that are extracted from a 24-hour dynamic 12-lead ECG recording captured by a Holter machine (DMS Holter Company, Stateline, NV, USA) at three campuses (Main Campus, Optical Valley Campus, and Sino-French New City Campus) of the Cardiac Function Examination Center (Division of Cardiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China). All the data were initially interpreted by primary cardiologists, then were further reviewed by three senior board-certified cardiologists to ensure the correctness of the base diagnostic labels. The cardiologist committees discussed by consensus the annotated records and provided a reference standard for model evaluation. Each atrial fibrillation(AF) episode included the accurately labeled start time and end time for patients with paroxysmal AF (PAF). The start and end times of each PAF episode were the corresponding time of the first atrial wave with an atrial rate greater than 350 beats/min and the corresponding time of the first P-wave with sinus rhythm after the termination of AF. Specifically, PAF episodes lasting more than 30 s were accurately labeled, and those lasting less than 30 s were labeled as accurately as possible. Moreover, each interval of premature beat or tachycardia was marked as “A” (atrium event) or “V” (ventricle event), and the long RR interval caused by QRS wave dropping was marked as “B” to further label Second-degree atrioventricular block. The 800 recordings included 200 whole-course AF (WAF), 200 PAF and 400 NAF recordings. For WAF patient data, whole recording included only AF signals. For NAF patients, the entire recording data had no AF signals but included normal sinus rhythm, sinus arrhythmia, atrial arrhythmia, ventricular arrhythmia, atrioventricular block and so on. The data of PAF patients included both AF episodes and NAF signals. Both WAF and PAF patients were considered to be AF patients. The type of each recording is indicated by its file name and the comments on the contents can be seen in the picture "Note.jpg".

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Mohd Fadhli Mohd Fauzi; Hanizah Mohd Yusoff; Nur Adibah Mat Saruan; Rosnawati Muhamad Robat (2023). Work-related activities during non-work time. [Dataset]. http://doi.org/10.1371/journal.pone.0241577.t002

Work-related activities during non-work time.

Related Article
Explore at:
37 scholarly articles cite this dataset (View in Google Scholar)
xlsAvailable download formats
Dataset updated
Jun 1, 2023
Dataset provided by
PLOS ONE
Authors
Mohd Fadhli Mohd Fauzi; Hanizah Mohd Yusoff; Nur Adibah Mat Saruan; Rosnawati Muhamad Robat
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Work-related activities during non-work time.

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