Background: Very young children, and those with disabilities and extensive oral pathology, who cannot be treated in the dental chair, require deep sedation or general anesthesia for dental treatment. Objective: The aim of this study is to describe and compare the oral health status in healthy and SHCN children and the treatments performed under deep sedation on an outpatient basis with a minimal intervention approach, and their impact on quality of life. Methods: A retrospective study between 2006 and 2018 was made. A total of 230 medical records of healthy and SHCN children were included. The data extracted were age, sex, systemic health status, reason for sedation, oral health status before sedation, treatments administered during sedation, and follow-up. The quality of life after deep sedation of 85 children was studied through parental questionnaires. Descriptive and inferential analyses were made. Results: Of the 230 children, 47.4% were healthy and 52.6% were SHCN. The median age was 7.10 ± 3.40 years (5.04 ± 2.42 in healthy children and 8.95 ± 3.09 in SHCN children). The main reason for sedation was poor handling in the dental chair (99.5%). The most frequent pathologies were caries (90.9%) and pulp pathology (67.8%). Healthy children had more teeth affected by decay and with pulp involvement. Patients aged <6 years received more pulpectomies and pulpotomies. After treatment, parents stated that children were more rested and less irascible, ate better, increased in weight, and had improved dental aesthetics. Conclusions: Differences in treatments carried out did not depend on the general health status or the failure rate but on age, with more pulp treatments in healthy children who were younger, and more extractions near to the age of physiological turnover in children with SHCN who were older. Intervention under deep sedation with a minimally invasive treatments approach met the expectations of parents and guardians, as it improved the children’s quality of life.
Background The application of data-driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output is preventing a wider adoption of these methods. To address this gap, we introduce and describe ORIENTATE, a software for automated application of machine learning classification algorithms by clinical practitioners lacking specific technical skills. ORIENTATE allows the selection of features and the target variable, then automatically generates a number of classification models and cross-validates them, finding the best model and evaluating it. It also implements a custom feature selection algorithm for systematic searches of the best combination of predictors for a given target variable. Finally, it outputs a comprehensive report with graphs that facilitates the explanation of the classification model results, using global interpretation methods, and an interface for the prediction of new input samples. Feature relevance and interaction plots provided by ORIENTATE allow to use it for statistical inference, which can replace and/or complement classical statistical studies. Results Its application to a dataset with healthy and special health care needs (SHCN) children, treated under deep sedation, was discussed as case study. On the example dataset, despite its small size, the feature selection algorithm found a set of features able to predict the need for a second sedation with a f1 score of 0.83 and a ROC (AUC) of 0.92. Eight predictive factors for both populations were found and ordered by the relevance assigned to them by the model. A discussion of how to derive inferences from the relevance and interaction plots and a comparison with a classical study is also provided. Conclusions ORIENTATE automatically finds suitable features and generates accurate classifiers which can be used in preventive tasks. In addition, researchers without specific skills on data methods can use it for the application of machine learning classification and as a complement to classical studies for inferential analysis of features. In the case study, a high prediction accuracy for a second sedation in SHCN children was achieved. The analysis of the relevance of the features showed that the number of teeth with pulpar treatments at the first sedation is a predictive factor for a second sedation.
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