يعرض 781 - 800 نتائج من 9,650 نتيجة بحث عن 'significant ((((gap decrease) OR (((we decrease) OR (greater decrease))))) OR (mean decrease))', وقت الاستعلام: 0.49s تنقيح النتائج
  1. 781
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  4. 784

    Demographics of the enrolled patients. حسب Yuka Kasai (21354922)

    منشور في 2025
    الموضوعات:
  5. 785

    Overhead view of the eye drop aid. حسب Yuka Kasai (21354922)

    منشور في 2025
    الموضوعات:
  6. 786
  7. 787
  8. 788
  9. 789
  10. 790
  11. 791
  12. 792

    Sound stress exposure prolonged the period of decreased withdrawal threshold after complete Freund’s adjuvant (CFA) injection. حسب Satoka Kasai (3861115)

    منشور في 2025
    "…(B) 50% withdrawal threshold in CFA-treated mice exposed to sound stress. They showed a significant decrease in 50% withdrawal threshold on day 7 after CFA injection, and CFA-treated mice exposed to sound stress showed a significant decrease in 50% withdrawal threshold during days 7–21 after CFA injection (CFA, on day 7, ****<i>P</i> < 0.001 vs day 0, on day 10, ***<i>P</i> < 0.001 vs day0; CFA + stress, day 7, 10, 14, and 21, ****<i>P</i> < 0.0001 vs day 0, Dunnett’s test). …"
  13. 793
  14. 794

    Model selection based on best fit. حسب Angelina Mageni Lutambi (22097223)

    منشور في 2025
    "…The results showed that malaria incidence decreased with greater variance across Tanzania. Mean malaria incidence decreased from 0.347 (95% CI: 0.336, 0.357) in 2000 to 0.118 (95% CI: 0.114, 0.122) in 2020, relative to the increasing insecticide-treated bednets (ITNs) coverage (0.037; 95% CI: 0.036, 0.039 in 2000 to 0.496; 95% CI: 0.476, 0.517 in 2020). …"
  15. 795

    Preference for the EIA – conjoint results. حسب Mehdi Mourali (10170245)

    منشور في 2025
    "…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …"
  16. 796

    Sample attribute table. حسب Mehdi Mourali (10170245)

    منشور في 2025
    "…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …"
  17. 797

    Subgroup analysis – Political affiliation. حسب Mehdi Mourali (10170245)

    منشور في 2025
    "…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …"
  18. 798

    Sample scenario description. حسب Mehdi Mourali (10170245)

    منشور في 2025
    "…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …"
  19. 799

    AMCEs – Pooled across scenarios. حسب Mehdi Mourali (10170245)

    منشور في 2025
    "…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …"
  20. 800

    Methodological flowchart. حسب Mehdi Mourali (10170245)

    منشور في 2025
    "…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …"