Kashmola, Muhanad Ali and Mustafa, Nazih Shaban and Mohd Ibrahim, Mohamad Shafiq and Jamhari, Nor Ain and Mat Daud, Nor Hanan and UNSPECIFIED
(2025)
A retrospective study on the evaluation, management, and outcomes of oral mucosal lesions in patients at the oral medicine clinic.
IIUM Journal of Orofacial and Health Sciences, 6 (2).
pp. 187-198.
E-ISSN 2735-0584
Abstract
Oral mucosal lesions (OMLs) are prevalent across different populations worldwide, with varying frequencies in different regions. This study aims to evaluate the frequency of OMLs and analyse their distribution concerning age, gender, race, and systemic conditions. A retrospective cross-sectional study was conducted on patients who attended the Oral Medicine students’ polyclinic, International Islamic University Malaysia, over a period of seven years. Data from a total of 85 patients were collected and analysed using descriptive statistical methods. The analysis included patient demographics such as age, gender, race, systemic diseases, as well as the types and sites of oral mucosal lesions (OMLs). OMLs were categorized into four main groups: ulcers, white lesions, infections, and others. Among the data collected from eighty-five patients, the prevalence of oral mucosal lesions was found to be higher in females than in males. The most common lesions were ulcers (65.9%), followed by infections (19.4%), white lesions (13.6%), and others (12.6%). Ulcers were predominantly found in individuals aged 15–30 years, whereas white lesions, infections, and other types were more frequent in the 31–60 age group. No significant association was found between systemic conditions and lesion types. Ulcers commonly appeared in multiple locations (37.5%), white lesions were most frequently found on the buccal mucosa (35.7%), infections were primarily observed on the hard palate (40.0%), and other lesions occurred most often on the labial mucosa (30.7%). Ulcers were the most prevalent type of oral mucosal lesions; however, no significant correlation was found between OMLs and systemic conditions
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