# Overall Positive Agreement

For the positive: real positive/real positive positive/false positive positive /False Positive Predictive Values: real positable/real negative/false negative/real negative/false positive Positive: true positive/true negative – false negative addition total consent percentage: true positive – true negative / total To be clear, there are two new tests: (1) tests for the SARS-CoV-2 virus itself, and (2 tests) for the virus. Each of these markers is now being analyzed by several methods that will be quickly approved by the FDA as part of the Emergency Use Authorization (EEA). Methods for the virus are generally, but not all, based on PCR, and methods for antibodies essentially fall into the category of serology tests. PCR and other nucleic acids or molecular methods are usually performed in a section of the laboratory, depending on where the instruments of these technologies are already in place. Serological tests are usually performed in another section of the laboratory. With the introduction of simple side flow tests, tests are also carried out in point-of-care situations. All of these tests are qualitative tests, which means they have a medical decision point (Cutoff) to classify the result as positive or negative. The share of the overall agreement (in) is the proportion of cases for which Councillors 1 and 2 agree. In other words, positive and negative forecast values (APP) are the proportions of positive and negative results in statistics and diagnostic tests, which are real positive or negative results.  PpV and NPV describe the performance of a diagnostic test or other statistical indicator. A high result can be interpreted as indicating the accuracy of such a statistic.

PPV and NPV are not inherent in the test (as the actual positive rate and actual negative rate are real); they also depend on prevalence.  App and NPV may be derived from the Bayes theorem. In Table 2, the share of Category I-specific agreements is 2nii ps (i) – ———. (6) nor. Consider, for example, an epidemiological application in which a positive assessment of a positive diagnosis for a very rare disease corresponds — a, say, one with a prevalence of 1 in 1,000,000. Here, we may not be very impressed when Po is very high — even above .99. This result is almost exclusively due to an agreement on the absence of disease; We are not informed directly if the diagnosticians agree on the occurrence of diseases. The Small Positive Forecast Value (APP – 10%) indicates that many of the positive results of this test method are false positive results. It will therefore be necessary to follow each positive result with a more reliable test in order to obtain a more accurate assessment of the issue of the existence of cancer. However, such a test can be useful if it is inexpensive and practical. Rather, the strength of the FOB screen test lies in its negative forecast value which, if negative for a person, gives us great confidence that its negative result is true. (a) The number of results for which both tests are positive; (b) number of results for which the candidate method is positive, but the comparison is negative; (c) number of results for which the candidate method is negative, but the comparison is positive; d – number of results for which both methods are negative.

For a given case with two or more binary ratings (positive/negative), you can indicate n and m the number of ratings or the number of positive ratings. In this particular case, there are chords in pairs of pairs of positive notes and x -m (n – 1) possibilities for such an agreement. If we calculate x and y for each case and add the two terms in all cases, then the sum of x is divided by the sum of y the share of the specific positive match in the whole sample.