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significant predictor » significant predictors (Expand Search), significant reduction (Expand Search), significant factor (Expand Search)
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Showing 1 - 20 results of 5,824 for search '(( significant predictor decrease ) OR ( significant based decrease ))', query time: 0.52s Refine Results
  1. 1
    Logistics regression COVID-19 vaccine predictors.

    Logistics regression COVID-19 vaccine predictors. by Adaobi Uchenna Mosanya (20384248)

    Published 2024
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  2. 2
    TBSS results showing significant AxD and RD changes at baseline and the follow-up.

    TBSS results showing significant AxD and RD changes at baseline and the follow-up. by Sewon Lim (21989785)

    Published 2025
    “…<p>The regions indicated in green represent the mean white matter skeleton, whereas the regions indicated in red represent the regions with increased AxD (first row) and RD (second row). The decreased diffusivity metrics (AxD and RD) exhibited no statistically significant differences at <i><i>P</i></i> < 0.05 after correcting for multiple comparisons (FWE). …”
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  3. 3
    Predictors of SCD.

    Predictors of SCD. by Iftikhar Ali Ch (20783753)

    Published 2025
    “…AMT group had a significantly higher incidence of SCD compared to those on DAPT (3.1% vs 0.8%; OR = 3.831, 95% CI: 1.961–7.519; p < 0.001). …”
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  4. 4
    Multivariate regression analysis to explain the decrease in visibility and palpability.

    Multivariate regression analysis to explain the decrease in visibility and palpability. by Miharu Matsumoto (21304390)

    Published 2025
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  5. 5
    Logistic regression for influenza results predictors.

    Logistic regression for influenza results predictors. by Reem Eid (20889732)

    Published 2025
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  6. 6
    Logistic regression for RSV results predictors.

    Logistic regression for RSV results predictors. by Reem Eid (20889732)

    Published 2025
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  7. 7
    Identification of ACADM, ANGPTL4, and NFKB2 as significant predictors of OS in the TCGA-KIRC cohort.

    Identification of ACADM, ANGPTL4, and NFKB2 as significant predictors of OS in the TCGA-KIRC cohort. by Yu Zhang (12946)

    Published 2025
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  8. 8
    General characteristics of relative handgrip strength at baseline.

    General characteristics of relative handgrip strength at baseline. by Yeo Ju Sohn (18536198)

    Published 2024
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  9. 9
    This dataset includes detailed information for 22,016 individuals.

    This dataset includes detailed information for 22,016 individuals. by Yeo Ju Sohn (18536198)

    Published 2024
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  10. 10
    Flow diagram of participants meeting the inclusion and exclusion criteria.

    Flow diagram of participants meeting the inclusion and exclusion criteria. by Yeo Ju Sohn (18536198)

    Published 2024
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  11. 11
    Initial participants by wave*.

    Initial participants by wave*. by Yeo Ju Sohn (18536198)

    Published 2024
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  12. 12
    These files contain the STATA program codes used in the study.

    These files contain the STATA program codes used in the study. by Yeo Ju Sohn (18536198)

    Published 2024
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  13. 13
    Generalized estimating equation model covariance structure analysis.

    Generalized estimating equation model covariance structure analysis. by Yeo Ju Sohn (18536198)

    Published 2024
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  14. 14
    Raw dataset collected from the KLoSA from 2006 to 2020.

    Raw dataset collected from the KLoSA from 2006 to 2020. by Yeo Ju Sohn (18536198)

    Published 2024
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  15. 15
    General characteristics of research participants.

    General characteristics of research participants. by Yeo Ju Sohn (18536198)

    Published 2024
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  16. 16
    Generalized estimation equation model analysis of diabetes mellitus incidence according to relative handgrip strength.

    Generalized estimation equation model analysis of diabetes mellitus incidence according to relative handgrip strength. by Yeo Ju Sohn (18536198)

    Published 2024
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  17. 17
    Trends analysis in diabetes mellitus diagnosis according to relative handgrip strength.

    Trends analysis in diabetes mellitus diagnosis according to relative handgrip strength. by Yeo Ju Sohn (18536198)

    Published 2024
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  18. 18
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to federal parks and protected areas (PPA), including national parks, national monuments, and national forests included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with a diameter of 2.5 microns.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to federal parks and protected areas (PPA), i... by Kira Minehart (21029571)

    Published 2025
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  19. 19
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to local parks and protected areas (PPA), including city parks, county parks, and regional parks included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to local parks and protected areas (PPA), inc... by Kira Minehart (21029571)

    Published 2025
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  20. 20
    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to state parks and protected areas (PPA), including state parks and state recreation areas included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.

    Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to state parks and protected areas (PPA), inc... by Kira Minehart (21029571)

    Published 2025
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