Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial
BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess wh...
Ausführliche Beschreibung
Autor*in: |
Agoritsas, Thomas [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2010 |
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Anmerkung: |
© Society of General Internal Medicine 2010 |
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Übergeordnetes Werk: |
Enthalten in: Journal of general internal medicine - New York, NY : Springer, 1986, 26(2010), 4 vom: 04. Nov., Seite 373-378 |
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Übergeordnetes Werk: |
volume:26 ; year:2010 ; number:4 ; day:04 ; month:11 ; pages:373-378 |
Links: |
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DOI / URN: |
10.1007/s11606-010-1540-5 |
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Katalog-ID: |
SPR021104913 |
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520 | |a BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. | ||
700 | 1 | |a Courvoisier, Delphine S. |4 aut | |
700 | 1 | |a Combescure, Christophe |4 aut | |
700 | 1 | |a Deom, Marie |4 aut | |
700 | 1 | |a Perneger, Thomas V. |4 aut | |
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10.1007/s11606-010-1540-5 doi (DE-627)SPR021104913 (SPR)s11606-010-1540-5-e DE-627 ger DE-627 rakwb eng Agoritsas, Thomas verfasserin aut Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society of General Internal Medicine 2010 BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. Courvoisier, Delphine S. aut Combescure, Christophe aut Deom, Marie aut Perneger, Thomas V. aut Enthalten in Journal of general internal medicine New York, NY : Springer, 1986 26(2010), 4 vom: 04. Nov., Seite 373-378 (DE-627)320456978 (DE-600)2006784-7 1525-1497 nnns volume:26 year:2010 number:4 day:04 month:11 pages:373-378 https://dx.doi.org/10.1007/s11606-010-1540-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 26 2010 4 04 11 373-378 |
spelling |
10.1007/s11606-010-1540-5 doi (DE-627)SPR021104913 (SPR)s11606-010-1540-5-e DE-627 ger DE-627 rakwb eng Agoritsas, Thomas verfasserin aut Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society of General Internal Medicine 2010 BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. Courvoisier, Delphine S. aut Combescure, Christophe aut Deom, Marie aut Perneger, Thomas V. aut Enthalten in Journal of general internal medicine New York, NY : Springer, 1986 26(2010), 4 vom: 04. Nov., Seite 373-378 (DE-627)320456978 (DE-600)2006784-7 1525-1497 nnns volume:26 year:2010 number:4 day:04 month:11 pages:373-378 https://dx.doi.org/10.1007/s11606-010-1540-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 26 2010 4 04 11 373-378 |
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10.1007/s11606-010-1540-5 doi (DE-627)SPR021104913 (SPR)s11606-010-1540-5-e DE-627 ger DE-627 rakwb eng Agoritsas, Thomas verfasserin aut Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society of General Internal Medicine 2010 BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. Courvoisier, Delphine S. aut Combescure, Christophe aut Deom, Marie aut Perneger, Thomas V. aut Enthalten in Journal of general internal medicine New York, NY : Springer, 1986 26(2010), 4 vom: 04. Nov., Seite 373-378 (DE-627)320456978 (DE-600)2006784-7 1525-1497 nnns volume:26 year:2010 number:4 day:04 month:11 pages:373-378 https://dx.doi.org/10.1007/s11606-010-1540-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 26 2010 4 04 11 373-378 |
allfieldsGer |
10.1007/s11606-010-1540-5 doi (DE-627)SPR021104913 (SPR)s11606-010-1540-5-e DE-627 ger DE-627 rakwb eng Agoritsas, Thomas verfasserin aut Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society of General Internal Medicine 2010 BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. Courvoisier, Delphine S. aut Combescure, Christophe aut Deom, Marie aut Perneger, Thomas V. aut Enthalten in Journal of general internal medicine New York, NY : Springer, 1986 26(2010), 4 vom: 04. Nov., Seite 373-378 (DE-627)320456978 (DE-600)2006784-7 1525-1497 nnns volume:26 year:2010 number:4 day:04 month:11 pages:373-378 https://dx.doi.org/10.1007/s11606-010-1540-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 26 2010 4 04 11 373-378 |
allfieldsSound |
10.1007/s11606-010-1540-5 doi (DE-627)SPR021104913 (SPR)s11606-010-1540-5-e DE-627 ger DE-627 rakwb eng Agoritsas, Thomas verfasserin aut Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society of General Internal Medicine 2010 BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. Courvoisier, Delphine S. aut Combescure, Christophe aut Deom, Marie aut Perneger, Thomas V. aut Enthalten in Journal of general internal medicine New York, NY : Springer, 1986 26(2010), 4 vom: 04. Nov., Seite 373-378 (DE-627)320456978 (DE-600)2006784-7 1525-1497 nnns volume:26 year:2010 number:4 day:04 month:11 pages:373-378 https://dx.doi.org/10.1007/s11606-010-1540-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 26 2010 4 04 11 373-378 |
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A Randomized Trial</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">© Society of General Internal Medicine 2010</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. 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Agoritsas, Thomas Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial |
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Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial |
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Agoritsas, Thomas Courvoisier, Delphine S. Combescure, Christophe Deom, Marie Perneger, Thomas V. |
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does prevalence matter to physicians in estimating post-test probability of disease? a randomized trial |
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Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial |
abstract |
BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. © Society of General Internal Medicine 2010 |
abstractGer |
BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. © Society of General Internal Medicine 2010 |
abstract_unstemmed |
BACKGROUND The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN Randomized trial. PARTICIPANTS Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors. © Society of General Internal Medicine 2010 |
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title_short |
Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial |
url |
https://dx.doi.org/10.1007/s11606-010-1540-5 |
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Courvoisier, Delphine S. Combescure, Christophe Deom, Marie Perneger, Thomas V. |
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Courvoisier, Delphine S. Combescure, Christophe Deom, Marie Perneger, Thomas V. |
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doi_str |
10.1007/s11606-010-1540-5 |
up_date |
2024-07-03T20:25:16.226Z |
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|
score |
7.399617 |