CASE REPORT: This report highlights two rare variants of Monteggia fracture-dislocation seen in children. The first case was a 12-year old girl alleged to have fallen from a 15- feet tall tree and sustaining a combined type III Monteggia injury with ipsilateral Type II Salter-Harris injury of distal end radius with a metaphyseal fracture of the distal third of the ulna. The second case was a 13-year old who had sustained a closed fracture of atypical Type I Monteggia hybrid lesion, in a road traffic accident.
CONCLUSION: This report highlights the rare variants of Monteggia fracture dislocation which could have been missed without proper clinical examinations and radiographs.
METHODS: A total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated.
RESULTS: The application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS.
CONCLUSION: The decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students' learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students.