Improving Diagnostic Accuracy: Simple Statistical Nomograms to Interpret Medical Literature
Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presenc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bliss, David [verfasserIn] |
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Englisch |
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2010 |
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© Société Internationale de Chirurgie 2010 |
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Enthalten in: World Journal of Surgery - Springer-Verlag, 1996, 34(2010), 7 vom: 20. Apr., Seite 1401-1405 |
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| Übergeordnetes Werk: |
volume:34 ; year:2010 ; number:7 ; day:20 ; month:04 ; pages:1401-1405 |
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10.1007/s00268-010-0574-5 |
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| 520 | |a Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. | ||
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10.1007/s00268-010-0574-5 doi (DE-627)SPR003428907 (SPR)s00268-010-0574-5-e DE-627 ger DE-627 rakwb eng Bliss, David verfasserin aut Improving Diagnostic Accuracy: Simple Statistical Nomograms to Interpret Medical Literature 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Société Internationale de Chirurgie 2010 Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. Appendicitis (dpeaa)DE-He213 Positive Predictive Value (dpeaa)DE-He213 Negative Predictive Value (dpeaa)DE-He213 Mesenteric Ischemia (dpeaa)DE-He213 High Positive Predictive Value (dpeaa)DE-He213 Diggs, Brian aut Matar, Marla aut Enthalten in World Journal of Surgery Springer-Verlag, 1996 34(2010), 7 vom: 20. Apr., Seite 1401-1405 (DE-627)SPR003391159 nnns volume:34 year:2010 number:7 day:20 month:04 pages:1401-1405 https://dx.doi.org/10.1007/s00268-010-0574-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA AR 34 2010 7 20 04 1401-1405 |
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10.1007/s00268-010-0574-5 doi (DE-627)SPR003428907 (SPR)s00268-010-0574-5-e DE-627 ger DE-627 rakwb eng Bliss, David verfasserin aut Improving Diagnostic Accuracy: Simple Statistical Nomograms to Interpret Medical Literature 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Société Internationale de Chirurgie 2010 Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. Appendicitis (dpeaa)DE-He213 Positive Predictive Value (dpeaa)DE-He213 Negative Predictive Value (dpeaa)DE-He213 Mesenteric Ischemia (dpeaa)DE-He213 High Positive Predictive Value (dpeaa)DE-He213 Diggs, Brian aut Matar, Marla aut Enthalten in World Journal of Surgery Springer-Verlag, 1996 34(2010), 7 vom: 20. Apr., Seite 1401-1405 (DE-627)SPR003391159 nnns volume:34 year:2010 number:7 day:20 month:04 pages:1401-1405 https://dx.doi.org/10.1007/s00268-010-0574-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA AR 34 2010 7 20 04 1401-1405 |
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10.1007/s00268-010-0574-5 doi (DE-627)SPR003428907 (SPR)s00268-010-0574-5-e DE-627 ger DE-627 rakwb eng Bliss, David verfasserin aut Improving Diagnostic Accuracy: Simple Statistical Nomograms to Interpret Medical Literature 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Société Internationale de Chirurgie 2010 Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. Appendicitis (dpeaa)DE-He213 Positive Predictive Value (dpeaa)DE-He213 Negative Predictive Value (dpeaa)DE-He213 Mesenteric Ischemia (dpeaa)DE-He213 High Positive Predictive Value (dpeaa)DE-He213 Diggs, Brian aut Matar, Marla aut Enthalten in World Journal of Surgery Springer-Verlag, 1996 34(2010), 7 vom: 20. Apr., Seite 1401-1405 (DE-627)SPR003391159 nnns volume:34 year:2010 number:7 day:20 month:04 pages:1401-1405 https://dx.doi.org/10.1007/s00268-010-0574-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA AR 34 2010 7 20 04 1401-1405 |
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10.1007/s00268-010-0574-5 doi (DE-627)SPR003428907 (SPR)s00268-010-0574-5-e DE-627 ger DE-627 rakwb eng Bliss, David verfasserin aut Improving Diagnostic Accuracy: Simple Statistical Nomograms to Interpret Medical Literature 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Société Internationale de Chirurgie 2010 Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. Appendicitis (dpeaa)DE-He213 Positive Predictive Value (dpeaa)DE-He213 Negative Predictive Value (dpeaa)DE-He213 Mesenteric Ischemia (dpeaa)DE-He213 High Positive Predictive Value (dpeaa)DE-He213 Diggs, Brian aut Matar, Marla aut Enthalten in World Journal of Surgery Springer-Verlag, 1996 34(2010), 7 vom: 20. Apr., Seite 1401-1405 (DE-627)SPR003391159 nnns volume:34 year:2010 number:7 day:20 month:04 pages:1401-1405 https://dx.doi.org/10.1007/s00268-010-0574-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA AR 34 2010 7 20 04 1401-1405 |
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10.1007/s00268-010-0574-5 doi (DE-627)SPR003428907 (SPR)s00268-010-0574-5-e DE-627 ger DE-627 rakwb eng Bliss, David verfasserin aut Improving Diagnostic Accuracy: Simple Statistical Nomograms to Interpret Medical Literature 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Société Internationale de Chirurgie 2010 Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. Appendicitis (dpeaa)DE-He213 Positive Predictive Value (dpeaa)DE-He213 Negative Predictive Value (dpeaa)DE-He213 Mesenteric Ischemia (dpeaa)DE-He213 High Positive Predictive Value (dpeaa)DE-He213 Diggs, Brian aut Matar, Marla aut Enthalten in World Journal of Surgery Springer-Verlag, 1996 34(2010), 7 vom: 20. Apr., Seite 1401-1405 (DE-627)SPR003391159 nnns volume:34 year:2010 number:7 day:20 month:04 pages:1401-1405 https://dx.doi.org/10.1007/s00268-010-0574-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA AR 34 2010 7 20 04 1401-1405 |
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Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. © Société Internationale de Chirurgie 2010 |
| abstractGer |
Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. © Société Internationale de Chirurgie 2010 |
| abstract_unstemmed |
Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. Discussion These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease. © Société Internationale de Chirurgie 2010 |
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Improving Diagnostic Accuracy: Simple Statistical Nomograms to Interpret Medical Literature |
| url |
https://dx.doi.org/10.1007/s00268-010-0574-5 |
| remote_bool |
true |
| author2 |
Diggs, Brian Matar, Marla |
| author2Str |
Diggs, Brian Matar, Marla |
| ppnlink |
SPR003391159 |
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c |
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false |
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false |
| doi_str |
10.1007/s00268-010-0574-5 |
| up_date |
2024-07-03T19:26:56.427Z |
| _version_ |
1803587227678146560 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR003428907</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519101536.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201001s2010 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00268-010-0574-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR003428907</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00268-010-0574-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bliss, David</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving Diagnostic Accuracy: Simple Statistical Nomograms to Interpret Medical Literature</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Société Internationale de Chirurgie 2010</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex. Methods Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature. Results Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data. 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7.3993607 |