2 datasets found
  1. H

    Replication Data for: Pluralistic Collapse (ASR, 2020)

    • dataverse.harvard.edu
    Updated Jun 8, 2020
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    Daniel DellaPosta (2020). Replication Data for: Pluralistic Collapse (ASR, 2020) [Dataset]. http://doi.org/10.7910/DVN/5DPHSO
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 8, 2020
    Dataset provided by
    Harvard Dataverse
    Authors
    Daniel DellaPosta
    License

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

    Description

    Replication data and code for "Pluralistic Collapse: The 'Oil Spill' Model of Mass Opinion Polarization." Code should be run in this order: Step 1 - Variable_Recodes.do (Stata; this can also be skipped if you just want to start with the R files and use the prepared GSS_Recoded.dta file) Step 2 - Create Pairs Step 3 - Gather Correlations Step 4 - Model Correlations Step 5 - Observed Networks Step 6 - Bootstrapping (parts1, 2, and 3 in that order) Step 7 - Plot Results Note that several of these steps are computationally intensive. The "parallel" package in R can be very useful for purposes of speeding up the computation. Note: In a previous version of this dataset, I forgot to include the Step 5 file which is used to generate the observed (non-bootstrapped) networks. This is now included, and the subsequent files have been relabeled.

  2. Summary data and analyses.

    • plos.figshare.com
    xls
    Updated Nov 15, 2024
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    Wensi Ouyang; Guimei Guo; Jie Xia; Changwei Zhao; Xiaoling Zhou (2024). Summary data and analyses. [Dataset]. http://doi.org/10.1371/journal.pone.0313265.t003
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    xlsAvailable download formats
    Dataset updated
    Nov 15, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Wensi Ouyang; Guimei Guo; Jie Xia; Changwei Zhao; Xiaoling Zhou
    License

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

    Description

    BackgroundMinimally invasive treatment options for osteonecrosis of the femoral head (ONFH) have been a prominent area of research in recent years. Arthroscopic-assisted treatments have been applied in the clinical management of ONFH; however, high-quality evidence verifying their effectiveness and safety is still lacking.ObjectiveTo systematically assess the clinical efficacy and safety of arthroscopic-assisted core decompression (AACD) in treating ONFH.MethodsA comprehensive literature search was conducted in PubMed, Web of Science, EMBASE, Cochrane Library, Chinese National Knowledge Infrastructure, China Science and Technology Journal Database, WanFang, and the Chinese BioMedical Literature Database, from inception to June 25, 2024. We identified randomized controlled trials and non-randomized controlled studies on AACD for the treatment of ONFH based on predefined inclusion and exclusion criteria. A meta-analysis was performed using Review Manager 5.4.1 and Stata 17.0 software. The analyzed outcomes included operative time, intraoperative blood loss, length of hospital stay, postoperative femoral head collapse rate, Harris hip score, and postoperative complication rate. The Grades of Recommendations, Assessment, Development, and Evaluations (GRADE) system was used to assess the quality of evidence for the outcome indicators.ResultsA total of fourteen studies were included in this meta-analysis, comprising 1,063 patients-541 in the core decompression (CD) group and 522 in the AACD group. The meta-analysis revealed no significant differences between the two groups in terms of intraoperative blood loss, length of hospital stay, 12-month postoperative Harris hip score, or overall postoperative complication rate (P > 0.05). However, the AACD group had a longer operative time (MD = 31.19, 95% Cl: 5.32 to 57.07, P = 0.02) and a lower overall postoperative femoral head collapse rate (RR = 0.49, 95% Cl: 0.27 to 0.89, P = 0.02) compared with the CD group. Additionally, the AACD group showed significant improvements in Harris hip scores at 3 months (MD = 6.39, 95% Cl: 5.44 to 7.33, P < 0.00001), 6 months (MD = 7.56, 95% Cl: 6.63 to 8.49, P < 0.00001), ≥ 24 months (MD = 7.00, 95% Cl: 4.80 to 9.21, P < 0.00001), and at the last follow-up (MD = 6.89, 95% Cl: 5.30 to 8.48, P < 0.00001) compared to the CD group. The GRADE evidence assessment indicated that the overall postoperative complication rate was supported by moderate-quality evidence, while the evidence for operative time, intraoperative blood loss, postoperative femoral head collapse rate, and Harris hip score was of low quality. The evidence for length of hospital stay was deemed very low quality.ConclusionThis meta-analysis suggests that AACD is an effective and safe treatment for patients with ONFH. However, due to the limited quantity and quality of the included studies, these results should be interpreted with caution. Further high-quality studies are recommended to confirm these findings.

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Daniel DellaPosta (2020). Replication Data for: Pluralistic Collapse (ASR, 2020) [Dataset]. http://doi.org/10.7910/DVN/5DPHSO

Replication Data for: Pluralistic Collapse (ASR, 2020)

Related Article
Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jun 8, 2020
Dataset provided by
Harvard Dataverse
Authors
Daniel DellaPosta
License

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

Description

Replication data and code for "Pluralistic Collapse: The 'Oil Spill' Model of Mass Opinion Polarization." Code should be run in this order: Step 1 - Variable_Recodes.do (Stata; this can also be skipped if you just want to start with the R files and use the prepared GSS_Recoded.dta file) Step 2 - Create Pairs Step 3 - Gather Correlations Step 4 - Model Correlations Step 5 - Observed Networks Step 6 - Bootstrapping (parts1, 2, and 3 in that order) Step 7 - Plot Results Note that several of these steps are computationally intensive. The "parallel" package in R can be very useful for purposes of speeding up the computation. Note: In a previous version of this dataset, I forgot to include the Step 5 file which is used to generate the observed (non-bootstrapped) networks. This is now included, and the subsequent files have been relabeled.

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