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The aim of this study was to compare the clinical efficiency of a diode laser as an adjunct to scaling and root planing (SRP) in the treatment of chronic periodontitis patients, and also to evaluate the changes in the clinical parameters such as clinical attachment level in teeth with periodontal pockets and blood reactive oxygen metabolites. Method and Materials: A total of thirty patients (mean age 38.2 years) with chronic periodontitis were selected for this study. The patients were randomly assigned into two groups of 15 patients each, as the control group and test group. The control group received only conventional SRP and the test group received conventional SRP and diode laser (GaAlAs)-assisted pocket debridement. The clinical parameters (Plaque Index, bleeding on probing, probing pocket depth, and clinical attachment level) were recorded at baseline and day 60, and the serum levels of reactive oxygen metabolites were estimated at baseline, day 30, and day 60 for both the groups. Results: When the groups were compared, there was statistically significant improvement in Plaque Index score, decrease in bleeding on probing and probing pocket depth, and gain in clinical attachment level (P < .001) in both the groups from baseline to day 60. There was significant reduction in reactive oxygen metabolites in both the groups from baseline to day 30 and day 60 (P < .001). However no statistically significant changes were present between the treatment groups from baseline to day 60 in terms of clinical parameters and blood reactive oxygen metabolites. Conclusion: From the results observed in this study it can be concluded that use of diode laser as an adjunct to SRP did not provide any significant difference compared to use of SRP alone in terms of clinical parameters and reactive oxygen metabolites. [ABSTRACT FROM AUTHOR]

Importance: The overwhelming majority of fetal and neonatal deaths occur in low- and middle-income countries. Fetal and neonatal risk assessment tools may be useful to predict the risk of death., Objective: To develop risk prediction models for intrapartum stillbirth and neonatal death., Design, Setting, and Participants: This cohort study used data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Global Network for Women's and Children's Health Research population-based vital registry, including clinical sites in South Asia (India and Pakistan), Africa (Democratic Republic of Congo, Zambia, and Kenya), and Latin America (Guatemala). A total of 502 648 pregnancies were prospectively enrolled in the registry., Exposures: Risk factors were added sequentially into the data set in 4 scenarios: (1) prenatal, (2) predelivery, (3) delivery and day 1, and (4) postdelivery through day 2., Main Outcomes and Measures: Data sets were randomly divided into 10 groups of 3 analysis data sets including training (60%), test (20%), and validation (20%). Conventional and advanced machine learning modeling techniques were applied to assess predictive abilities using area under the curve (AUC) for intrapartum stillbirth and neonatal mortality., Results: All prenatal and predelivery models had predictive accuracy for both intrapartum stillbirth and neonatal mortality with AUC values 0.71 or less. Five of 6 models for neonatal mortality based on delivery/day 1 and postdelivery/day 2 had increased predictive accuracy with AUC values greater than 0.80. Birth weight was the most important predictor for neonatal death in both postdelivery scenarios with independent predictive ability with AUC values of 0.78 and 0.76, respectively. The addition of 4 other top predictors increased AUC to 0.83 and 0.87 for the postdelivery scenarios, respectively., Conclusions and Relevance: Models based on prenatal or predelivery data had predictive accuracy for intrapartum stillbirths and neonatal mortality of AUC values 0.71 or less. Models that incorporated delivery data had good predictive accuracy for risk of neonatal mortality. Birth weight was the most important predictor for neonatal mortality.