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Objective To assess the efficacy of using a bone substitute material (BSM) in the fixture–socket gap in patients undergoing tooth extraction and immediate implant placement. Materials and methods MEDLINE, EMBASE, and CENTRAL databases were searched for randomized controlled trials (RCTs). RCTs were screened for eligibility, and data were extracted by two authors independently. Risk of bias (ROB) was assessed using Cochrane's ROB tool 2.0. Primary outcomes were implant failure, overall complications, and soft‐tissue esthetics. Secondary outcomes were vertical buccal bone resorption, vertical interproximal bone resorption, horizontal buccal bone resorption, and mid‐buccal mucosal recession. Meta‐analysis was performed using random‐effects model with generic inverse variance weighing. GRADE was used to grade the certainty of the evidence. Results After screening 19 544 potentially eligible references, 20 RCTs were included in this review, with a total of 848 patients (916 sites). Most included RCTs were deemed of some concerns (53%) or at low (38%) risk of bias, except for overall complications (high ROB). Implant failure did not differ significantly RR = 0.92 (confidence intervals [CI] 0.34 to 2.46) between using a BSM compared with not using a BSM (NoBSM). BSM use resulted in less horizontal buccal bone resorption (MD = −0.52 mm [95% CI −0.74 to −0.30]), a higher esthetic score (MD = 1.49 [95% CI 0.46 to 2.53]), but also more complications (RR = 3.50 [95% CI 1.11 to 11.1] compared with NoBSM. Too few trials compared types of BSMs against each other to allow for pooled analyses. The certainty of the evidence was considered moderate for all outcomes except implant failure (low), overall complications (very low), and vertical interproximal bone resorption (very low). Conclusion BSM use during immediate implant placement reduces horizontal buccal bone resorption and improves the periimplant soft‐tissue esthetics. Although BSM use increases the risk of predominantly minor complications.

PurposeThe objective of this study is to develop and validate a neural‐based modelling methodology applicable to site‐specific short‐ and medium‐term ozone concentration forecasting. A novel modelling technique utilizing two feed forward artificial neural networks (FFNN) is developed to improve the performance of time series predictions.Design/methodology/approachAir pollution and meteorological data were collected for one year in two locations in Kuwait. The hourly averages of the data were processed to generate a covariance matrix and analyzed to generate the principal component method. A two‐FFNN model is then used to predict the actual data.FindingsThe newly developed model improves the prediction accuracy over the conventional method. Owing to the presence of noise and other minor disturbances in the data, shorter‐range modelling gives better modelling results.Originality/valueA novel modelling technique is developed to predict the time series of zone concentration.

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