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Showing 2,281 - 2,300 results of 22,509 for search '(( significant decrease decrease ) OR ( significant association created ))', query time: 0.45s Refine Results
  1. 2281
    Demographic characteristics of study participants.

    Demographic characteristics of study participants. by Lijuan Liu (136685)

    Published 2025
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  2. 2282
    Results of Analysis of Co-variance (ANCOVA) for change in parameter predicted by treatment group with pre-treatment value as a covariate. * p < 0.05; ** p < 0.01.

    Results of Analysis of Co-variance (ANCOVA) for change in parameter predicted by treatment group with pre-treatment value as a covariate. * p < 0.05; ** p < 0.01. by Lijuan Liu (136685)

    Published 2025
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  3. 2283
    Comparison of treatment effects between premolar extraction and mandibular third molar extraction groups. * p < 0.05; ** p < 0.01.

    Comparison of treatment effects between premolar extraction and mandibular third molar extraction groups. * p < 0.05; ** p < 0.01. by Lijuan Liu (136685)

    Published 2025
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  4. 2284
    Comparison of upper and lower arch crowding between premolar extraction group and the mandibular third molar extraction groups.

    Comparison of upper and lower arch crowding between premolar extraction group and the mandibular third molar extraction groups. by Lijuan Liu (136685)

    Published 2025
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  5. 2285
    Intraoperative facial and intraoral photographs of the case with mandibular third molar extraction.

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

    Published 2025
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  6. 2286
    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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  7. 2287
    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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  8. 2288
    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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  9. 2289
    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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  10. 2290
    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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  11. 2291
    Office characteristics and RW adoption.

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

    Published 2025
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  12. 2292
    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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  13. 2293
    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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  14. 2294
    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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  15. 2295
    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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  16. 2296
    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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  17. 2297
    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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  18. 2298
    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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  19. 2299
    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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  20. 2300
    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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