3 datasets found
  1. f

    Data from: Prediction of long-term survival in patients with metastatic...

    • tandf.figshare.com
    docx
    Updated Jun 1, 2023
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    Ivar Hompland; Øyvind Sverre Bruland; Toto Hølmebakk; Jan Peter Poulsen; Stephan Stoldt; Kirsten Sundby Hall; Kjetil Boye (2023). Prediction of long-term survival in patients with metastatic gastrointestinal stromal tumor: analysis of a large, single-institution cohort [Dataset]. http://doi.org/10.6084/m9.figshare.5050288.v1
    Explore at:
    docxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Ivar Hompland; Øyvind Sverre Bruland; Toto Hølmebakk; Jan Peter Poulsen; Stephan Stoldt; Kirsten Sundby Hall; Kjetil Boye
    License

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

    Description

    Background: A subset of patients with metastatic GIST become long-term survivors, and a more precise prediction of outcome could improve clinical decision-making. Material and methods: One-hundred and thirty-three patients diagnosed with metastatic GIST from 1995 to 2013 were identified from the sarcoma database at Oslo University Hospital. Clinical data prospectively registered in the database were supplemented with retrospective review of medical records. Factors associated with survival were analyzed using Kaplan–Meier curves, log-rank test, univariate and multivariate Cox regression analyses. Results: One-hundred and fifteen patients with metastatic GIST were included in the final study cohort. Median overall survival (OS) was 6.9 years (95% CI 5.6–8.3). Factors associated with long-term survival in univariate analysis were good baseline performance status (ECOG ≤1; p 

  2. f

    The inter-class correlation coefficient (ICC) and Weighted Kappa values for...

    • plos.figshare.com
    xls
    Updated Jun 20, 2023
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    Ziba Gandomkar; Somphone Siviengphanom; Mo’ayyad Suleiman; Dennis Wong; Warren Reed; Ernest U. Ekpo; Dong Xu; Sarah J. Lewis; Karla K. Evans; Jeremy M. Wolfe; Patrick C. Brennan (2023). The inter-class correlation coefficient (ICC) and Weighted Kappa values for measuring the intra-observer variability in Round 1 and Round 2 and the ICC, Weighted Kappa, and Fleiss Kappa for measuring the inter-observer variability of the gist signal. [Dataset]. http://doi.org/10.1371/journal.pone.0284605.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 20, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ziba Gandomkar; Somphone Siviengphanom; Mo’ayyad Suleiman; Dennis Wong; Warren Reed; Ernest U. Ekpo; Dong Xu; Sarah J. Lewis; Karla K. Evans; Jeremy M. Wolfe; Patrick C. Brennan
    License

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

    Description

    For inter-reader ICC value and Fleiss Kappa, the 95% confidence interval (CI) is also reported.

  3. f

    Water level data @ Gists Creek 2w Sevierville

    • floodalert.app
    • sobos.at
    Updated Nov 18, 2024
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    SOBOS GmbH, Shared Environment (2024). Water level data @ Gists Creek 2w Sevierville [Dataset]. https://floodalert.app/nl/river.php?river=Gists%20Creek%202w%20Sevierville
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    Dataset updated
    Nov 18, 2024
    Dataset authored and provided by
    SOBOS GmbH, Shared Environment
    License

    https://creativecommons.org/publicdomain/by/4.0/https://creativecommons.org/publicdomain/by/4.0/

    Area covered
    Sevierville, Gists Creek,
    Description

    Online service voor waterstandinformatie voor BE, NL, USA, CA, UK, IE, DE, AT, CH en Zuid-Tirol. SMS- en e-mailwaarschuwing kan geactiveerd worden. Waarschuwt u bij overstromingen.

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

Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Ivar Hompland; Øyvind Sverre Bruland; Toto Hølmebakk; Jan Peter Poulsen; Stephan Stoldt; Kirsten Sundby Hall; Kjetil Boye (2023). Prediction of long-term survival in patients with metastatic gastrointestinal stromal tumor: analysis of a large, single-institution cohort [Dataset]. http://doi.org/10.6084/m9.figshare.5050288.v1

Data from: Prediction of long-term survival in patients with metastatic gastrointestinal stromal tumor: analysis of a large, single-institution cohort

Related Article
Explore at:
docxAvailable download formats
Dataset updated
Jun 1, 2023
Dataset provided by
Taylor & Francis
Authors
Ivar Hompland; Øyvind Sverre Bruland; Toto Hølmebakk; Jan Peter Poulsen; Stephan Stoldt; Kirsten Sundby Hall; Kjetil Boye
License

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

Description

Background: A subset of patients with metastatic GIST become long-term survivors, and a more precise prediction of outcome could improve clinical decision-making. Material and methods: One-hundred and thirty-three patients diagnosed with metastatic GIST from 1995 to 2013 were identified from the sarcoma database at Oslo University Hospital. Clinical data prospectively registered in the database were supplemented with retrospective review of medical records. Factors associated with survival were analyzed using Kaplan–Meier curves, log-rank test, univariate and multivariate Cox regression analyses. Results: One-hundred and fifteen patients with metastatic GIST were included in the final study cohort. Median overall survival (OS) was 6.9 years (95% CI 5.6–8.3). Factors associated with long-term survival in univariate analysis were good baseline performance status (ECOG ≤1; p 

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