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Showing 2,501 - 2,520 results of 21,342 for search '(( significant decrease decrease ) OR ( significant sources decrease ))', query time: 0.37s Refine Results
  1. 2501
    Pretreatment facial, intraoral photographs, and radiographs of the case with premolar tooth extraction.

    Pretreatment facial, intraoral photographs, and radiographs of the case with premolar tooth extraction. by Lijuan Liu (136685)

    Published 2025
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  2. 2502
    Pretreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction.

    Pretreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

    Published 2025
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  3. 2503
    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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  4. 2504
    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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  5. 2505
    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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  6. 2506
    Office characteristics and RW adoption.

    Office characteristics and RW adoption. by Yasuhiko Deguchi (3167955)

    Published 2025
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  7. 2507
    An infographic outlining the data used for each analysis.

    An infographic outlining the data used for each analysis. by Shelby L. Sturrock (10751675)

    Published 2025
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  8. 2508
    Average difference in mean daily exercise minutes in the week before and after the specified software transition, using different minimum data thresholds.

    Average difference in mean daily exercise minutes in the week before and after the specified software transition, using different minimum data thresholds. by Shelby L. Sturrock (10751675)

    Published 2025
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  9. 2509
    Effect of each software transition and any hardware transition on daily active calories (negative binomial models with a random intercept per participant) among participants with activity data for all 7 days before and all 7 days after transitioning between the indicated software versions, or between two different hardware versions.

    Effect of each software transition and any hardware transition on daily active calories (negative binomial models with a random intercept per participant) among participants with a... by Shelby L. Sturrock (10751675)

    Published 2025
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  10. 2510
    Effect of each software transition and any hardware transition on daily minutes of exercise (negative binomial models with a random intercept per participant) among participants with activity data for all 7 days before and all 7 days after transitioning between the indicated software versions or between two different hardware versions.

    Effect of each software transition and any hardware transition on daily minutes of exercise (negative binomial models with a random intercept per participant) among participants wi... by Shelby L. Sturrock (10751675)

    Published 2025
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  11. 2511
    Average difference in mean daily exercise minutes in the week before and the week after transitioning between the indicated software versions.

    Average difference in mean daily exercise minutes in the week before and the week after transitioning between the indicated software versions. by Shelby L. Sturrock (10751675)

    Published 2025
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  12. 2512
    Newly occurring LTSA-MD cases in workers for over a month.

    Newly occurring LTSA-MD cases in workers for over a month. by Yasuhiko Deguchi (3167955)

    Published 2025
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  13. 2513
    Average difference in mean daily active calories in the week before and after the specified software transition, using different minimum data thresholds.

    Average difference in mean daily active calories in the week before and after the specified software transition, using different minimum data thresholds. by Shelby L. Sturrock (10751675)

    Published 2025
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  14. 2514
    Average difference in mean daily active calories in the week before and the week after transitioning between the indicated software versions.

    Average difference in mean daily active calories in the week before and the week after transitioning between the indicated software versions. by Shelby L. Sturrock (10751675)

    Published 2025
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  15. 2515
    Difference in the number of LTSA between FY 2020 and FY 2019.

    Difference in the number of LTSA between FY 2020 and FY 2019. by Yasuhiko Deguchi (3167955)

    Published 2025
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  16. 2516
    Participant characteristics, overall and for each transition, of primary analytic sample (activity data for all 14 days of the respective transition period).

    Participant characteristics, overall and for each transition, of primary analytic sample (activity data for all 14 days of the respective transition period). by Shelby L. Sturrock (10751675)

    Published 2025
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  17. 2517
    BMI curve for LSES.

    BMI curve for LSES. by Krystal Hunter (6820052)

    Published 2025
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  18. 2518
    BMI groups by SES.

    BMI groups by SES. by Krystal Hunter (6820052)

    Published 2025
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  19. 2519
    BMISES_Data_Part2.

    BMISES_Data_Part2. by Krystal Hunter (6820052)

    Published 2025
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  20. 2520
    Logistic regression for LSES population.

    Logistic regression for LSES population. by Krystal Hunter (6820052)

    Published 2025
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