Factors affecting the outcome of the coronally advanced flap procedure: a Bayesian network analysis.

Background: The aim of this study was to explore possible causal relationships among several variables in the coronally advanced flap for root coverage procedure using structural learning of Bayesian networks.Methods: Sixty consecutive patients with maxillary buccal recessions (≥2 mm) were enrolled. All defects were treated with the coronally advanced flap procedure. Age, gender, smoking habits, recession depth, width of keratinized tissue, probing depth, distance between the incisal margin and the cemento-enamel junction, root sensitivity, and distance between the gingival margin and the cemento-enamel junction were recorded and calculated for all patients at baseline, immediately after surgery, and at 6 months after surgery. A structural learning algorithm of Bayesian networks was used.Results: The distance between the gingival margin and the cemento-enamel junction immediately after surgery was affected by the baseline recession depth; deeper recessions were associated with a more apical location of th...

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