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Showing 1 - 20 results of 7,780 for search '(( third ((((age decrease) OR (_ decrease))) OR (larger decrease)) ) OR ( _ patient decrease ))', query time: 0.47s Refine Results
  1. 1
    Shares of antibiotic substances on total prescription in UTI treatment (N39.0, N30.0) by sex and age group in 2019 (female: n = 238.670 prescriptions; male: n = 40.641 prescriptions).

    Shares of antibiotic substances on total prescription in UTI treatment (N39.0, N30.0) by sex and age group in 2019 (female: n = 238.670 prescriptions; male: n = 40.641 prescription... by Thomas Hanslmeier (19949694)

    Published 2024
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  2. 2
    Number of antibiotic prescriptions in female and male patients with UTI (N30.0 and/or N39.0).

    Number of antibiotic prescriptions in female and male patients with UTI (N30.0 and/or N39.0). by Thomas Hanslmeier (19949694)

    Published 2024
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  3. 3
    Shares of antibiotic substances on total prescription by quarter from 2013 to 2019 for female and male patients with UTI (N39.0, N30.0)(female: n = 1.681.255 prescriptions; male: n = 269.151prescriptions).

    Shares of antibiotic substances on total prescription by quarter from 2013 to 2019 for female and male patients with UTI (N39.0, N30.0)(female: n = 1.681.255 prescriptions; male: n... by Thomas Hanslmeier (19949694)

    Published 2024
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  4. 4
    Shares of specialist groups prescribing 1<sup>st</sup>, 2<sup>nd</sup>, and 3<sup>rd</sup> antibiotic prescriptions in UTI treatment of females (N39.0, N30.0) in 2018 and 2019.

    Shares of specialist groups prescribing 1<sup>st</sup>, 2<sup>nd</sup>, and 3<sup>rd</sup> antibiotic prescriptions in UTI treatment of females (N39.0, N30.0) in 2018 and 2019. by Thomas Hanslmeier (19949694)

    Published 2024
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  5. 5
    Shares of antibiotic substances on total prescription in female patients with UTI (N39.0, N30.0) by physician specialties (GPs: n = 1.299.641 prescriptions; gynecologists n = 141.814 prescriptions; urologists n = 182.100 prescriptions).

    Shares of antibiotic substances on total prescription in female patients with UTI (N39.0, N30.0) by physician specialties (GPs: n = 1.299.641 prescriptions; gynecologists n = 141.8... by Thomas Hanslmeier (19949694)

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  6. 6
    Comparison of 1<sup>st</sup>, 2<sup>nd</sup>, and 3<sup>rd</sup> antibiotic prescription.

    Comparison of 1<sup>st</sup>, 2<sup>nd</sup>, and 3<sup>rd</sup> antibiotic prescription. by Thomas Hanslmeier (19949694)

    Published 2024
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  7. 7
    UTI diagnoses by sex and physician specialty (2013–2019).

    UTI diagnoses by sex and physician specialty (2013–2019). by Thomas Hanslmeier (19949694)

    Published 2024
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  8. 8
    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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  9. 9
    Posttreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction.

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

    Published 2025
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  10. 10
    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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  11. 11
    Additional file 7 of Decreased complement 4 and interleukin-10 as biomarkers in aqueous humour for non-exudative age-related macular degeneration: a case control study

    Additional file 7 of Decreased complement 4 and interleukin-10 as biomarkers in aqueous humour for non-exudative age-related macular degeneration: a case control study by Juliane Schikora (20871169)

    Published 2025
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  12. 12
    Additional file 3 of Decreased complement 4 and interleukin-10 as biomarkers in aqueous humour for non-exudative age-related macular degeneration: a case control study

    Additional file 3 of Decreased complement 4 and interleukin-10 as biomarkers in aqueous humour for non-exudative age-related macular degeneration: a case control study by Juliane Schikora (20871169)

    Published 2025
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  13. 13
    Superimposition of the cephalometric tracings obtained before and after treatment of case with mandibular third molar extraction.

    Superimposition of the cephalometric tracings obtained before and after treatment of case with mandibular third molar extraction. by Lijuan Liu (136685)

    Published 2025
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  14. 14
    Comparison of pretreatment cephalometric variables between premolar extraction and mandibular third molar extraction groups.

    Comparison of pretreatment cephalometric variables between premolar extraction and mandibular third molar extraction groups. by Lijuan Liu (136685)

    Published 2025
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  15. 15
    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)

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  16. 16
    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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  17. 17
    These subfigures show a visualization of the context embeddings for the DSVAE model with LS=2=4,CS=2. The first subfigure from the left shows three patient clusters, the second subfigure both patients and controls, the third colors context embeddings based on the subject’s age, and the last subfigure colors the context embeddings based on the subject’s cognitive score, CMINDs.

    These subfigures show a visualization of the context embeddings for the DSVAE model with LS=2=4,CS=2. The first subfigure from the left shows three patient clusters, the second sub... by Eloy Geenjaar (21533195)

    Published 2025
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  18. 18
    Flow diagram of patient selection for the study.

    Flow diagram of patient selection for the study. by Yusuke Fuchioka (22806178)

    Published 2025
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  19. 19
    Factors associated with AL score (multivariable regression analyses for the third follow-up).

    Factors associated with AL score (multivariable regression analyses for the third follow-up). by Eric Priedemann (21175492)

    Published 2025
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  20. 20
    Third.

    Third. by Cuicui Liu (1496260)

    Published 2024
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