Advances in Management of Class II Malocclusions

Although mandibular advancement by bilateral sagittal split osteotomy seems to be a good mandibular treatment option to treat skeletal class II malocclusion, it is less stable than setback; relapse depends on a wide range of patient‐centered and surgeon‐centered factors relating to the skill and experience of the surgeon, proper seating of the condyles, the exact amount of mandibular advancement, the tension of the muscles and soft tissues, the mandibular plane angle, and the patient\u27s age. In fact, patients with low and high mandibular plane angles have increased vertical and horizontal relapses, respectively. Nonsurgical management of class II malocclusion may be an option by which to effectively manage such cases. The present chapter discusses different treatment modalities for clinical management of class II malocclusion in growing and non‐growing patients

Management of Costochondral Graft Overgrowth Following Treatment of Condylar Ankylosis: A Case Report

Costochondral graft (CCG) is a common treatment modality for temporomandibular joint (TMJ) ankylosis. One of disadvantages of CCG is unpredictability of growth pattern and risk of overgrowth. This report illustrates management of a patient with CCG overgrowth. The patient was a girl, aged 7 years with severe facial asymmetry and TMJ ankylosis. The treatment comprised releasing of ankylotic mass and use of CCG for TMJ reconstruction. Four years later, the patient underwent overgrowth of the grafted side. Following clinical examination and scintigraphy, the grafted side was shaved to prevent more growth and the patient left to pass adolescent growth spurt. Ultimately, remnant deviation may be compensated by mild genioplasty and fat injectio

The Effect of Methylphenidate on Cervical Vertebral Maturation and Dental Age in Patients with Attention Deficit Hyperactivity Disorder

Statement of the Problem: It is postulated that attention deficit/hyperactivity disorder (ADHD) with or without medication has an inhibitory effect on the children’s growth and development. Purpose: This study aimed to assess the dental age and cervical vertebral maturation (CVM) stage in ADHD patients with or without medication. Materials and Method: This cross-sectional study evaluated the pretreatment panoramic and lateral cephalograms of 129 patients (70 males, 59 females aged 8-14 years). Demirjian index and Baccetti’s CVM index were used to determine the dental age and CVM stage, respectively. The subjects were evaluated in two groups of ADHD (case, n=59) and healthy individuals (control, n=70). The ADHD patients were divided into two groups of AWT (ADHD with Treatment, n=43) and AW (ADHD without treatment, n=16) based on the use of methylphenidate. Paired t-test was used to compare the mean dental age between the groups. Linear and ordered logistic regression models were used to detect differences between the groups. The association between dental and chronological age was assessed by using Pearson correlation coefficient (p< 0.05). Results: After age and sex adjustment, the skeletal maturity stage was found to be similar to the control group based on the presence of the disorder or use of medication (p= 0.711 and p= 0.436, respectively). Similarly, the patients’ dental age was similar to the controls in AW and AWT groups (p= 0.180 and p= 0.421, respectively). The correlation between dental age and chronological age was 0.79 in AWT, 0.88 in AW, and 0.88 in control group (p< 0.001 for all the three). Conclusion: After age and sex adjustment, the dental and skeletal age of ADHD patients with or without Methylphenidate treatment do no manifest a significan

Modeling Human Visual Search Performance on Realistic Webpages Using Analytical and Deep Learning Methods

Modeling visual search not only offers an opportunity to predict the usability of an interface before actually testing it on real users, but also advances scientific understanding about human behavior. In this work, we first conduct a set of analyses on a large-scale dataset of visual search tasks on realistic webpages. We then present a deep neural network that learns to predict the scannability of webpage content, i.e., how easy it is for a user to find a specific target. Our model leverages both heuristic-based features such as target size and unstructured features such as raw image pixels. This approach allows us to model complex interactions that might be involved in a realistic visual search task, which can not be easily achieved by traditional analytical models. We analyze the model behavior to offer our insights into how the salience map learned by the model aligns with human intuition and how the learned semantic representation of each target type relates to its visual search performance.Comment: the 2020 CHI Conference on Human Factors in Computing System

Including Errors and Errors Correction in a Cognitive User Model

We present a user error model that simulates a user's errors using an eyes and hands extension to cognitive architectures. We developed a complete interactive cognitive model that performs a spreadsheet task. This model is compared with an existing cognitive model that performs the same task in a different spreadsheet tool. Also, the predictions are compared to human data (N=23) on the same uninstrumented interfaces. The comparison suggests that the interactive cognitive model moves us closer to having a user model that can directly test interfaces by predicting human behavior and performing the task on the same interface that users interact. The error model also allows exploration of error detection, error correction, and different knowledge types

Visual Attention during E-Learning: Eye-tracking Shows that Making Salient Areas More Prominent Helps Learning in Online Tutors

In this study, we investigate how high- and low-performance learners (N=12) act differently while using a cognitive tutoring system. We examine three research questions: (1) Can we predict learners’ performance using only their visual attention (eye movement data)? (2) Can we predict learners’ performance from visual attention data and initial performance? (3) Are age, gender, first language, where they look, and the sequence of Areas of Interests (AOIs) significant factors in the learners’ performance? Learners more correctly answer questions taken from larger rather than smaller AOIs. Our results show that high-performance learners pay more attention to the content that contains answers to later questions. Surprisingly, the tutor did not change the learners’ visual search to a goal-oriented search. Our analyses can help instructional designers create a more productive learning experience because visual search behavior as part of a learner model with acceptable accuracy in early stages can be used in adaptive tutors. Additionally, we trained a classifier on the eye movement data to predict learners’ performance for each question. Its results provide a list of suggestions for designing more productive learning experiences, such as enticing user attention by increasing the size of the content that contains answers and changing the order of contents

Predicting Learning and Retention of a Complex Task Using a Cognitive Architecture

We use a model to explore the implications of ACT-R's learning and forgetting mechanisms to understand learning and retention on a complex task. The model performs a spreadsheet task that has 14 non-iterated subtasks. The model predicts a learning curve and knowledge decay for different learning stages. The model's learning curve fits the human data well for the first four trials without decay. When decay is examined, however, we have to make modifications to the retention equation for the model's predictions to match data and the shapes predicted by the other learning theories. To fix this anomaly, we modified the effect of time on decay (adjusting time outside the experiment to less than the effect of time in the experiment) and the strength of newly learned memories (less well known than the previous default value). From these results, we learn that training and testing have been confounded in many studies

Predicting Learning and Retention of a Complex Task Using a Cognitive Architecture

We use a model to explore the implications of ACT-R's learning and forgetting mechanisms to understand learning and retention on a complex task. The model performs a spreadsheet task that has 14 non-iterated subtasks. The model predicts a learning curve and knowledge decay for different learning stages. The model's learning curve fits the human data well for the first four trials without decay. When decay is examined, however, we have to make modifications to the retention equation for the model's predictions to match data and the shapes predicted by the other learning theories. To fix this anomaly, we modified the effect of time on decay (adjusting time outside the experiment to less than the effect of time in the experiment) and the strength of newly learned memories (less well known than the previous default value). From these results, we learn that training and testing have been confounded in many studies

Rapid, conservative, multidisciplinary miniscrew-assisted approach for treatment of mandibular fractures following plane crash

Mandibular fractures are among the most common facial injuries. This case report demonstrates the efficacy of simultaneous usage of miniscrews and direct bonding techniques without open reduction in an extensive traumatized patient. A 25-year-old girl with multiple injuries in the head and facial region 1 month after a plane crash accident was referred to manage the mandibular fractures. Due to the presence of multiple injuries, a conservative treatment of symphysiseal fracture was performed. In order to keep the fractured fragments of the mandible close together, the anterior teeth of the lower arch were tied by means of the orthodontic wire. Ten miniscrews were used to improve the anchorage units and also, settling the occlusion by means of light intermaxillary elastics. Following the active treatment, clinical and radiographic analysis showed satisfactory healing without any periodontal involvement of the teeth in the fracture line