## 4 Discussion
However, prevalence is of great importance in determining the predictive values, which are dependent on the sample size and prevalence. Sensitivity (S) is the probability that a patient with PI will have a positive test result. It is calculated as the quotient of true positives among the total number of patients. For PI, a sensitive test could negate the need for continuous radiographic examinations. High test sensitivity would allow clinicians to treat patients without the need to resort to other diagnostic tests. In our study, the sensitivity was 9.1%; in the study by [PERSON] et al. [15], the sensitivity was 85.14%. This is a very large difference. This difference may have occurred because the patients in the study by [PERSON] et al. [15] had PD and those in our study had PI. Another possible cause of the difference could be related to the enrichment of _Aggregatibacter actinomycetemcomitans_ in the samples in the study by [PERSON] et al. [15] because these bacteria are found in a low proportion in PD. Specificity (E) is the ability of a test to identify healthy individuals. It is a measure that does not depend on sample size. It is calculated as the quotient of true negatives (healthy individuals with a negative result) among healthy individuals. Specificity was high in both studies. Therefore, 100% of patients who test positive will have bacteria involved in PI. Our results are consistent with those reported by [PERSON] et al. [15]. No patient had a false-positive result in either of the two studies. Predictive values or post-test probabilities represent the probability that a patient has the disease once the test result is known. Positive predictive value (PPV) is defined as the probability that an individual with a positive test has the disease and corresponds to patients with positive tests among all positive tests. PPV depends on prevalence, specificity, and, to a lesser extent, sensitivity. In the study by [PERSON] et al. [15], the PPV was 1 or 100%, and in our study, the PPV was also 100%. This value indicates that 100% of positive tests identify the presence of target bacteria in patients with PD or PI, indicating the need to eradicate these germs. Negative predictive value (NPV) is defined as the probability that an individual with a negative test does not have the disease, i.e., the individual is truly healthy. In the study by [PERSON] et al. [15], the NPV was 63%, and in our study, the NPV was 50%. Although the values are close, the probabilities are different. In the study by [PERSON] et al. [15], a negative test indicated that 37% of the patients who had a negative test result actually had the disease. In our study, a negative test indicated that 50% of the individuals who had a negative test result actually had the disease, meaning that half of the patients with PI may not be diagnosed. The reason why this value is so high may be due to threshold levels for the detection of germs being too high. [PERSON] et al. [15] reported levels between species that ranged from 10\({}^{4}\) and 10\({}^{2}\) depending on the species. Another difference may be due to the use of enriched suspensions of bacteria because sometimes the amount of bacteria present was very low, and therefore, the authors enriched the samples with cultures saturated with bacteria, specifically _A. actinomycetemcomitans_. Finally, another possible cause is the different profiles of the patients; in the study by [PERSON] et al. [15], the patients had PDs, and in our study, the patients had PI. The bacterial load required to develop PI is much lower than that required to develop PDs. The probability of a result for an individual with a disease and an individual without a disease is often compared using likelihood ratios. The advantage of this index compared to PPV and NPV is that unlike these, likelihood ratios do not depend on the proportion of patients in the sample but only on test sensitivity and specificity, hence, its usefulness when comparing diagnostic tests. In the study by [PERSON] et al.