Microaesthetics of The Smile: Extraction vs. Non-extraction

OBJECTIVE: To compare microaesthetics in pre- and post-orthodontic cases, treated with non-extraction and extraction treatment and assessed whether the achieved microaesthetic parameters are comparable to the proposed norms. STUDY DESIGN: Quasi-experimental study. PLACE AND DURATION OF STUDY: Orthodontic Clinic, the Aga Khan University Hospital, Karachi, from January 2005 to December 2009. METHODOLOGY: Orthodontic records of 31 cases treated with non-extraction therapy and 26 cases treated with extraction of upper first premolars were selected. Patients were of Pakistani origin, aged between 12 to 30 years. Microaesthetics was assessed by measuring maxillary central incisor crown width-height ratio, connectors between the maxillary anterior sextant, gingival zenith level of the maxillary lateral incisor and golden percentage of the anterior teeth using the patients\u27 plaster models and intraoral frontal photographs. Measurements of the golden percentage were made using the software Adobe Photoshop, whereas all other parameters were measured on the plaster casts using a digital vernier caliper. Paired t-test, independent t-test and one sample t-test were used to make comparisons within the groups, between the groups, and to compare the posttreatment values with the proposed norms, respectively. Statistical significance level was set at p 0.05. RESULTS: A statistically significant improvement in the microaesthetic parameters was observed for both extraction and non-extraction subjects (p \u3c 0.05) after orthodontic treatment. Values closer to the proposed norms were achieved more readily in the non-extraction group. CONCLUSION: Microaesthetics of the smile is improved with orthodontic treatment. It is recommended that greater consideration be given to the microaesthetic parameters of the smile during the finishing stages particularly when utilizing extraction mechanics during orthodontic treatment

Cervical posture and skeletal malocclusions – Is there a link?

Background: The present study was conducted in order to determine cervical posture in different skeletal sagittal malocclusions as well as to assess whether a correlation existed between cervical posture and skeletal relationships.Methods: Cervical curvature and inclination of 63 subjects was assessed using their lateral cephalometric radiographs. Cervical inclination was assessed using the cervicohorizontal postural variables namely OPT/HOR and CVT/HOR whereas cervical curvature was determined by measuring the angle OPT/CVT. Sagittally, the subjects were also categorized into skeletal Class I, II and III based on the angle ANB. One way ANOVA was used for the comparison of cervical posture in different skeletal sagittal malocclusions. Pearson’s correlation was used to evaluate the correlation of cervical posture with different skeletal sagittal jaw relations. Statistical significance level was set at p≤0.05.Results: Statistically significant differences were found between the different skeletal malocclusions for the cervical curvature angle OPT/CVT (p=0.025). A weak correlation of cervical curvature angle OPT/CVT (r=0.305, p=0.016) with sagittal malocclusion was found.Conclusions: Skeletal sagittal malocclusions differ in their cervical postures, especially cervical curvature. Skeletal Class III subjects have significantly straighter cervical columns than skeletal Class I subjects. Cervical curvature is correlated with sagittal jaw relations

A Novel Hybrid Ensemble Clustering Technique for Student Performance Prediction

Educational Data Mining (EDM) is a branch of data mining that focuses on extraction of useful knowledge from data generated through academic activities at school, college or at university level. The extracted knowledge can help to perform the academic activities in a better way, so it is useful for students, parents and institutions themselves. One common activity in EDM is students grade prediction with an aim to identify weak or at-risk students. An early identification of such students helps to take supportive measures that may help students to improve. Among a vast number of approaches available in this field, this study mainly focuses on generating a smarter dataset through reduced feature set without compromising the number of records in it and then producing an approach which combines the strengths of classification and clustering for better prediction results. In this study it has been identified that individual features have distinct effect and that removing misclassified data can affect the overall results. Backward selection is adopted using Pearson correlation as a metric to produce smarter dataset with lesser attributes and better accuracy in prediction. After feature set selection, we have applied EMT (Ensemble Meta-Based Tree Model) classification on it to identify best performing classifiers from five families of classifiers. In hybrid approach, first the ensemble clustering is applied on smart dataset and then EMT classification is applied to reevaluate the un-clustered data, which gives a boost in performance and provides us an accuracy of 93%