{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:35:27Z","timestamp":1788870927263,"version":"build-2803163510"},"reference-count":39,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2013,7,1]],"date-time":"2013-07-01T00:00:00Z","timestamp":1372636800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001827","name":"Universiteit van Amsterdam","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001827","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Periodontology"],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    The present study aims to extend recent findings of a non\u2010linear model of the progression of periodontitis supporting the notion that aggressive periodontitis (AgP) and chronic periodontitis (CP) are distinct clinical entities. This approach is based on the implementation of recursive partitioning analysis (RPA) to evaluate a series of immunologic parameters acting as predictors of AgP and CP.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    RPA was applied to three population samples, that were retrieved from previous studies, using 17 immunologic parameters. The mean values of the parameters in control subjects were used as the cut\u2010off points. Leave\u2010one\u2010out cross\u2010validation (LOOCV) prediction errors were estimated in the proposed models, as well as the Kullback\u2010Leibler divergence (\n                    <jats:italic>D<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>KL<\/jats:italic>\n                    <\/jats:sub>\n                    ) of the distribution of positive results in AgP compared to CP and negative results in CP compared to AgP.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Seven classification trees were derived showing that the relationship of interleukin (IL)\u20104, IL\u20101, IL\u20102 has the highest potential to rule out or rule in AgP. On the other hand, immunoglobulin (Ig)A, IgM used to rule out AgP and cluster of differentiation 4 (CD4)\/CD8, CD20 used to rule in AgP showed the least LOOCV cost. Penalizing\n                    <jats:italic>D<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>KL<\/jats:italic>\n                    <\/jats:sub>\n                    with LOOCV cost promotes the IL\u20104, IL\u20101, IL\u20102 model for ruling out AgP, whereas the single CD4\/CD8 ratio with a lowered discrimination cut\u2010off point was used to rule in AgP.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    Although a test is unlikely to have both high sensitivity and high specificity, the use of immunologic parameters in the right model can efficiently complement a clinical examination for ruling out or ruling in AgP.\n                  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