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sas prediction model  (SAS institute)


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    SAS institute sas prediction model
    Graphical visualisation of the trade-off between patients with septic arthritis not receiving empirical antibiotics versus patients without septic arthritis receiving antibiotics when using the SAS <t>prediction</t> <t>model</t> <t>across</t> <t>different</t> <t>treatment</t> <t>thresholds</t> and the authors’ suggested cut-offs (2% and 10%). “Usual care” is defined by clinician’s choice to treat with empirical antibiotics in the cohort where 4 patients with septic arthritis did not receive intravenous antibiotics and 244 patients without septic arthritis received antibiotics (orange and green dot)
    Sas Prediction Model, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/sas prediction model/product/SAS institute
    Average 90 stars, based on 1 article reviews
    sas prediction model - by Bioz Stars, 2026-05
    90/100 stars

    Images

    1) Product Images from "Septic arthritis score (SAS) – a novel clinical prediction model for the probability of septic arthritis in the adult native knee"

    Article Title: Septic arthritis score (SAS) – a novel clinical prediction model for the probability of septic arthritis in the adult native knee

    Journal: BMC Infectious Diseases

    doi: 10.1186/s12879-025-11306-6

    Graphical visualisation of the trade-off between patients with septic arthritis not receiving empirical antibiotics versus patients without septic arthritis receiving antibiotics when using the SAS prediction model across different treatment thresholds and the authors’ suggested cut-offs (2% and 10%). “Usual care” is defined by clinician’s choice to treat with empirical antibiotics in the cohort where 4 patients with septic arthritis did not receive intravenous antibiotics and 244 patients without septic arthritis received antibiotics (orange and green dot)
    Figure Legend Snippet: Graphical visualisation of the trade-off between patients with septic arthritis not receiving empirical antibiotics versus patients without septic arthritis receiving antibiotics when using the SAS prediction model across different treatment thresholds and the authors’ suggested cut-offs (2% and 10%). “Usual care” is defined by clinician’s choice to treat with empirical antibiotics in the cohort where 4 patients with septic arthritis did not receive intravenous antibiotics and 244 patients without septic arthritis received antibiotics (orange and green dot)

    Techniques Used:



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    Image Search Results


    Graphical visualisation of the trade-off between patients with septic arthritis not receiving empirical antibiotics versus patients without septic arthritis receiving antibiotics when using the SAS prediction model across different treatment thresholds and the authors’ suggested cut-offs (2% and 10%). “Usual care” is defined by clinician’s choice to treat with empirical antibiotics in the cohort where 4 patients with septic arthritis did not receive intravenous antibiotics and 244 patients without septic arthritis received antibiotics (orange and green dot)

    Journal: BMC Infectious Diseases

    Article Title: Septic arthritis score (SAS) – a novel clinical prediction model for the probability of septic arthritis in the adult native knee

    doi: 10.1186/s12879-025-11306-6

    Figure Lengend Snippet: Graphical visualisation of the trade-off between patients with septic arthritis not receiving empirical antibiotics versus patients without septic arthritis receiving antibiotics when using the SAS prediction model across different treatment thresholds and the authors’ suggested cut-offs (2% and 10%). “Usual care” is defined by clinician’s choice to treat with empirical antibiotics in the cohort where 4 patients with septic arthritis did not receive intravenous antibiotics and 244 patients without septic arthritis received antibiotics (orange and green dot)

    Article Snippet: Fig. 3 Graphical visualisation of the trade-off between patients with septic arthritis not receiving empirical antibiotics versus patients without septic arthritis receiving antibiotics when using the SAS prediction model across different treatment thresholds and the authors’ suggested cut-offs (2% and 10%).

    Techniques:

    Participant characteristics, smartphone usage patterns, and smartphone addiction scores of 257 Brazilian university students

    Journal: Psicologia, Reflexão e Crítica : revista semestral do Departamento de Psicologia da UFRGS

    Article Title: Predictors of problematic smartphone use among university students

    doi: 10.1186/s41155-020-00147-8

    Figure Lengend Snippet: Participant characteristics, smartphone usage patterns, and smartphone addiction scores of 257 Brazilian university students

    Article Snippet: Finally, the last model (multiple R = 0.57)—with demographic characteristics, loneliness, social apps importance, and smartphone model—predicted 32.2% of the SAS-BR variance.

    Techniques:

    Correlation table of the correlations between SAS-BR scores, UCLA-BR scores, social apps importance scores, and using smartphone for work importance

    Journal: Psicologia, Reflexão e Crítica : revista semestral do Departamento de Psicologia da UFRGS

    Article Title: Predictors of problematic smartphone use among university students

    doi: 10.1186/s41155-020-00147-8

    Figure Lengend Snippet: Correlation table of the correlations between SAS-BR scores, UCLA-BR scores, social apps importance scores, and using smartphone for work importance

    Article Snippet: Finally, the last model (multiple R = 0.57)—with demographic characteristics, loneliness, social apps importance, and smartphone model—predicted 32.2% of the SAS-BR variance.

    Techniques:

    Hierarchical regression analysis predicting problematic smartphone use, their standardized and unstandardized coefficients, the standard error (SE), and tolerance

    Journal: Psicologia, Reflexão e Crítica : revista semestral do Departamento de Psicologia da UFRGS

    Article Title: Predictors of problematic smartphone use among university students

    doi: 10.1186/s41155-020-00147-8

    Figure Lengend Snippet: Hierarchical regression analysis predicting problematic smartphone use, their standardized and unstandardized coefficients, the standard error (SE), and tolerance

    Article Snippet: Finally, the last model (multiple R = 0.57)—with demographic characteristics, loneliness, social apps importance, and smartphone model—predicted 32.2% of the SAS-BR variance.

    Techniques: