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{"sheet":"Leaf Plot","index":0,"n_rows":183,"n_cols":22,"rows_kept":183,"cut":false,"hidden":false,"merged":[[8,21,191,24],[1,2,1,24],[0,2,0,24],[2,2,2,24]]}
[null,null,"The leaf plot"]
[null,null,"What it is for: The leaf plot is designed to help you interpret the impact of a positive or negative test result, or the presence or absence of a clinical finding, by seeing how that alters the chance that a patient has a particular diagnosis. It is an aid to interpretting the impact of that result, according to the sensitivity and specificity of the particular additional test or sign."]
[null,null,"How to plot it: Simply enter the sensitivity and specificity values of the test or sign into the boxes below:"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Sensitivity =",0.97]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Specificity =",0.65]
[]
[]
["Sensitivity      ","False-positive rate","Specificity","False-negataive rate","Prevalence (prev)","Proportion of True Positives at that prev","Proportion of False Positives at that prev","Proportion of True Negatives at that prev","Proportion of False Negatives at that prev","PPV = TP/(TP+FP)   at that prev","NPV = TN/(TN+FN)     at that prev","1-NPV                   at that prev",null,"Prev","POS test","NEG test","Diagonal",null,null,null,null,"How to interpret it:                                                                                                                                                                                                                                                                                                                                                                                  1) Estimate the a-priori risk.  The diagonal dotted line shows your starting estimate of the chances of the patient having a particular diagnosis, based upon all the information available before doing the test, such as age, sex, past medical history, lifestyle and other specific risk factors, and that is where you should look for the impact of the test result (for example, if you are doing a screening test on somebody without any special risk of the condition, it will be the same as the prevalence of the condition in the normal population, but it will be much higher when your history and examination leads you to suspect a particular diagnosis in most patients you are considering).                                                                                                                                                                                                                                                                                                                                                                                       2) See how the test will alter that risk.  If the test is positive (or the sign is present), the probability of the diagnosis being true will increase vertically to the level indicated by the red line. If the test is negative (or the sign is absent), the probability will fall vertically to the level of the blue line."]
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