Background The latest trend in scientific literature review is to scrutinise the practices of false or biased reporting of findings, which is rightly termed as ‘spin’. In recent years, accelerated tooth movement has gained attention from the orthodontic community, but the findings still remain unclear and controversial. Objectives To estimate the frequency of distorted claims and over-interpretation of abstracts of systematic reviews related to accelerated orthodontic tooth movement. The objective was to differentiate the type of claim and to determine its prevalence. Methods A literature search was performed using the Cochrane library and the top five most prominent orthodontic journals for systematic reviews on accelerated orthodontics were identified by applying appropriate key words. According to pre-set selection criteria, only systematic reviews published between January 2010 and September 2021 were included. The selected articles were scrutinised for the assigned exclusion criteria. The articles were finally scanned for false claims by two independent reviewers. The identified claims fell into either the categories of misleading interpretation, misleading reporting or misleading extrapolation. The obtained data were tabulated and analysed using the one-way ANOVA statistical test to indicate the difference between the different types of reported claims. Results There were 98 systematic reviews identified in total, of which 59 articles met the selection criteria and 39 articles were excluded. Of the 59 included articles, 38 systematic reviews had exaggerated claims. Twenty-two of the reported claims came under the misleading reporting category, 10 fell under the misleading interpretation category and 6 came under the misleading extrapolation category. The difference noted between the reporting prevalence of different types of claim was statistically significant (P < 0.001). In misleading reporting, it was noted that most of the systematic reviews refrained from reporting the adverse effects of treatment. Conclusion The prevalence of exaggerated claims is high in the abstracts of systematic reviews related to accelerated orthodontic tooth movement. It is recommended that a clinician critically assess the claims presented in systematic reviews which are considered to be the hallmark articles of evidence-based practice. Orthodontists should be careful when applying the findings in clinical practice.

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Introduction: Artificial intelligence (AI) technology has transformed the way healthcare functions in the present scenario. In orthodontics, expert systems and machine learning have aided clinicians in making complex, multifactorial decisions. One such scenario is an extraction decision in a borderline case. Objective: The present in silico study was planned with the intention of building an AI model for extraction decisions in borderline orthodontic cases. Design: An observational analytical study. Setting: Department of Orthodontics, Hitkarini Dental College and Hospital, Madhya Pradesh Medical University, Jabalpur, India. Methods: An artificial neural network (ANN) model for extraction or non-extraction decisions in borderline orthodontic cases was constructed based on a supervised learning algorithm using the Python (version 3.9) Sci-Kit Learn library and feed-forward backpropagation method. Based on 40 borderline orthodontic cases, 20 experienced clinicians were asked to recommend extraction or non-extraction treatment. The decision of the orthodontist and the diagnostic records, including the selected extraoral and intra-oral features, model analysis and cephalometric analysis parameters, constituted the training dataset of AI. The built-in model was then tested using a testing dataset of 20 borderline cases. After running the model on the testing dataset, the accuracy, F1 score, precision and recall were calculated. Results: The present AI model showed an accuracy of 97.97% for extraction and non-extraction decision-making. The receiver operating curve (ROC) and cumulative accuracy profile showed a near-perfect model with precision, recall and F1 values of 0.80, 0.84 and 0.82 for non-extraction decisions and 0.90, 0.87 and 0.88 for extraction decisions. Limitation: As the present study was preliminary in nature, the dataset included was too small and population-specific. Conclusion: The present AI model gave accurate results in decision-making capabilities related to extraction and non-extraction treatment modalities in borderline orthodontic cases of the present population.

Introduction: In little more than a decade, social media has gone from being an entertainment source to a fully integrated part of nearly every aspect of daily life. This study aimed to provide an insight into how orthodontics-related social media posts are looked upon by the Indian population. Material and Methods: Orthodontics-related social media posts were analyzed for the number of likes, shares, and comments. Comments were also scrutinized for determining whether they were appreciation comments or enquiries related to orthodontic treatment and procedure. Posts were collected from 3 platforms: Twitter, Facebook, and Instagram. A mixed-methods approach was applied. First, all posts were structured according to a quantitative content analysis. Subsequently, qualitative analysis was performed to detect potential differences between the quality of response to posts on Twitter, Facebook, and Instagram. Using one-way ANOVA test, differences in the data were tabulated. A Chi- Square test was used to analyze the qualitative differences in the comments, which were scrutinized to check if they were appreciation comments or enquiries/doubts related to the posts. Results: There was a significant difference between the numbers of likes, shares, and comments. Appreciation comments were more in number than enquiries. Instagram had the maximum number of likes, followed by Facebook and Twitter ( P < .00001). Facebook had more shares in comparison to Twitter. Upon an analysis done on the number of comments, Facebook was found to have the highest number of comments, followed by Instagram and Twitter. All the results were significant, with P < .00001. Conclusion: It can be concluded that social media awareness related to orthodontics posts among Indians is gaining pace, and a lot can be achieved using these social media platforms to spread awareness related to orthodontic treatment.