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Specificity (SP) and sensitivity (SE) answer the question ‘what is the chance of a positive or negative test in response to the presence or absence of a clinical condition?’. Related to SP and SE are the diagnostic procedures of SNOUT and SPIN. SNOUT is the acronym for ‘Sensitive test when Negative rules OUT the disease’, SPIN for, ‘Specific test when Positive rules IN the disease’. SE and SP are incomplete because for clinical diagnosis, the question of concern should actually be: ‘what is the chance that the clinical condition will be present or absent in the context of a positive or negative test result?’. The latter statement is related to the concepts of Positive and Negative Predictive Value (PPV and NPV). However, PPV and NPV are predictive values not only dependent on SE and SP but also largely dependent on the prevalence in the examined population. Consequently, predictive values from one study should not be transferred to some other setting with a different prevalence. Prevalence affects PPV and NPV differently. PPV is increasing, while NPV decreases with the increase of the prevalence. This makes prevalence the nemesis in the application of the predictive values. Therefore, another variable has been introduced to evaluate the strength of a diagnostic test, namely the likelihood ratio. Likelihood ratios determine how much more likely a particular test result is among people who have the clinical condition of interest than it is among people who do not have the condition. LIKELIHOOD RATIO (LR) is the ratio of two probabilities. This letter illustrates the limitations of the concepts of SE, SP, NPV, PPV and the LRs in context of specific shoulder tests.

@article{baeyens_questioning_2019-1, title = {Questioning the “{SPIN} and {SNOUT}” rule in clinical testing}, volume = {9}, issn = {2057-0082}, url = {https://archivesphysiotherapy.biomedcentral.com/articles/10.1186/s40945-019-0056-5}, doi = {10.1186/s40945-019-0056-5}, abstract = {Specificity (SP) and sensitivity (SE) answer the question ‘what is the chance of a positive or negative test in response to the presence or absence of a clinical condition?’. Related to SP and SE are the diagnostic procedures of SNOUT and SPIN. SNOUT is the acronym for ‘Sensitive test when Negative rules OUT the disease’, SPIN for, ‘Specific test when Positive rules IN the disease’. SE and SP are incomplete because for clinical diagnosis, the question of concern should actually be: ‘what is the chance that the clinical condition will be present or absent in the context of a positive or negative test result?’. The latter statement is related to the concepts of Positive and Negative Predictive Value (PPV and NPV). However, PPV and NPV are predictive values not only dependent on SE and SP but also largely dependent on the prevalence in the examined population. Consequently, predictive values from one study should not be transferred to some other setting with a different prevalence. Prevalence affects PPV and NPV differently. PPV is increasing, while NPV decreases with the increase of the prevalence. This makes prevalence the nemesis in the application of the predictive values. Therefore, another variable has been introduced to evaluate the strength of a diagnostic test, namely the likelihood ratio. Likelihood ratios determine how much more likely a particular test result is among people who have the clinical condition of interest than it is among people who do not have the condition. LIKELIHOOD RATIO (LR) is the ratio of two probabilities. This letter illustrates the limitations of the concepts of SE, SP, NPV, PPV and the LRs in context of specific shoulder tests.}, language = {en}, number = {1}, urldate = {2020-06-10}, journal = {Archives of Physiotherapy}, author = {Baeyens, Jean-Pierre and Serrien, Ben and Goossens, Maggie and Clijsen, Ron}, month = dec, year = {2019}, pages = {4}, file = {Baeyens et al. - 2019 - Questioning the “SPIN and SNOUT” rule in clinical .pdf:/Users/neil.hawkins/Zotero/storage/YQW9SSWI/Baeyens et al. - 2019 - Questioning the “SPIN and SNOUT” rule in clinical .pdf:application/pdf}, }

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