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summary receiver operating characteristic sroc curves  (STATA Corporation)


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    Structured Review

    STATA Corporation summary receiver operating characteristic sroc curves
    The ability <t>of</t> <t>APIP</t> to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) <t>SROC</t> curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot
    Summary Receiver Operating Characteristic Sroc Curves, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 36852 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/summary+receiver+operating+characteristic+sroc+curves/STATA+12%2E0/pmc12541992-95-13-28
    Average 99 stars, based on 36852 article reviews
    summary receiver operating characteristic sroc curves - by Bioz Stars, 2026-10
    99/100 stars

    Images

    1) Product Images from "Clinical pathological significance and biological functions of APIP in hepatocellular carcinoma"

    Article Title: Clinical pathological significance and biological functions of APIP in hepatocellular carcinoma

    Journal: Cancer Cell International

    doi: 10.1186/s12935-025-04018-z

    The ability of APIP to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) SROC curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot
    Figure Legend Snippet: The ability of APIP to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) SROC curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot

    Techniques Used: Expressing

    Related Articles

    Generated:

    Article Title: Exploring the impact of insufficient thermal ablation on hepatocellular carcinoma: NDST2 overexpression mechanism and its role in facilitating growth and invasion of residual cancer cells.
    Article Snippet: Data analysis was carried out using GraphPad Prism 8, and statistical analysis employed the t-test for both groups. .. Standardized mean difference (SMD) forest plots and summary receiver operating characteristic (sROC) curves were generated using Stata 12, with a significance level set at p < 0.05. ..

    Article Title: Diagnostic accuracy of routine hematological biomarkers for complications and prognosis in bowel obstruction: a systematic review and meta-analysis
    Article Snippet: .. Summary Receiver Operating Characteristic (SROC) curves with 95% confidence regions and prediction regions were generated using midas command in Stata 14 [ ]. ..

    Expressing:

    Article Title: Clinical Significance and Prognostic Value of Lactate Dehydrogenase Expression in Cervical Cancer.
    Article Snippet: .. Expression and diagnostic data of LDHA in CC and noncancer cervical tissues were extracted from each data set to construct a Forest plot of the standard mean difference (SMD) using the meta package of R and summary receiver operating characteristic (SROC) curves using Stata v.14.0. ..

    Diagnostic Assay:

    Article Title: Clinical Significance and Prognostic Value of Lactate Dehydrogenase Expression in Cervical Cancer.
    Article Snippet: .. Expression and diagnostic data of LDHA in CC and noncancer cervical tissues were extracted from each data set to construct a Forest plot of the standard mean difference (SMD) using the meta package of R and summary receiver operating characteristic (SROC) curves using Stata v.14.0. ..

    Construct:

    Article Title: Clinical Significance and Prognostic Value of Lactate Dehydrogenase Expression in Cervical Cancer.
    Article Snippet: .. Expression and diagnostic data of LDHA in CC and noncancer cervical tissues were extracted from each data set to construct a Forest plot of the standard mean difference (SMD) using the meta package of R and summary receiver operating characteristic (SROC) curves using Stata v.14.0. ..

    Software:

    Article Title: Meta-analysis of the GALAD model for diagnosing primary hepatocellular carcinoma
    Article Snippet: BACKGROUND: Ever since the GALAD (gender-age-Lens culinaris agglutinin-reactive alpha-fetoprotein-alpha-fetoproteindes-gamma-carboxy prothrombin) logistic regression model was established to diagnose hepatocellular carcinoma (HCC), there has been no high-level evidence that evaluates and summarizes it.. OBJECTIVE: This meta-analysis was performed to assess the diagnostic ability of the GALAD model. METHODS: The following databases were systematically searched for original diagnostic studies on HCC: PubMed, Embase, Medline, the Web of Science, Cochrane Library, China National Knowledge Infrastructure Wanfang (China), Wiper and the Chinese BioMedical Literature Database.. After screening the search results according to our criteria, the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to evaluate the methodologic qualities, and statistical software were used to output the statistics.

    Article Title: Clinical pathological significance and biological functions of APIP in hepatocellular carcinoma
    Article Snippet: .. To assess the clinical utility of APIP in HCC, standardized mean difference (SMD), summary receiver operating characteristic (SROC) curves, and pooled sensitivity and specificity analyses were conducted using Stata 12.0 software, and receiver operating characteristic (ROC) curves was conducted using R v3.6.1 packages“pROC”. ..

    other:

    Article Title: The value of miRNAs as a biomarker for the diagnosis of gestational diabetes mellitus: a meta-analysis.
    Article Snippet: Statistical analysis Pooled sensitivity and specificity of the index test were performed using summary receiver operating characteristic (SROC) curves with STATA 12.0.



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    Image Search Results


    The ability of APIP to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) SROC curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot

    Journal: Cancer Cell International

    Article Title: Clinical pathological significance and biological functions of APIP in hepatocellular carcinoma

    doi: 10.1186/s12935-025-04018-z

    Figure Lengend Snippet: The ability of APIP to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) SROC curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot

    Article Snippet: To assess the clinical utility of APIP in HCC, standardized mean difference (SMD), summary receiver operating characteristic (SROC) curves, and pooled sensitivity and specificity analyses were conducted using Stata 12.0 software, and receiver operating characteristic (ROC) curves was conducted using R v3.6.1 packages“pROC”.

    Techniques: Expressing

    Summary receiver operator characteristic curve (SROC) of ML algorithms

    Journal: BMC Endocrine Disorders

    Article Title: Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis

    doi: 10.1186/s12902-025-01955-8

    Figure Lengend Snippet: Summary receiver operator characteristic curve (SROC) of ML algorithms

    Article Snippet: Fig. 7 Summary receiver operator characteristic curve (SROC) of ML algorithms

    Techniques: