MATERIALS AND METHODS: The sample consisted of 63 dentate subjects (21 Malays, 21 Chinese, and 21 Indians) who were chosen based on the inclusion criteria. Two models were made using irreversible hydrocolloid impressions, and an average of the value was obtained. Lingual frenum was recorded in function. Casts were fabricated with dental stone. AALF was marked and the vertical distance was measured using a caliper.
RESULTS: In Malays, the mean and standard deviation of the vertical distance were 14.2 ± 0.9 mm, with a range of 12.3-16.0 mm; in Chinese were 14.4 ± 0.9 mm, with a range of 12.0-16.9 mm; and in Indians were 15.1 ± 1.0 mm, with a range of 13.0-17.2 mm. The difference among the three races regarding the distance between AALF and the incisal edge of mandibular central incisors was statistically significant (P < 0.05). Among the three races, Malays and Indians have the greatest mean difference.
CONCLUSION: According to the results of the current study, the distance between AALF and the incisal edge of mandibular central incisors might be a proper criterion for the initial adjustment of occlusal rims. The values obtained from three different races were significantly different from one another, hence a different range of values was used to establish occlusal height for different races.
SUBJECTS AND METHODS: A prospective study of 355 participants, including 280 with oral lesions/variants was conducted. Adults aged ≥18 treated at tertiary referral centres were included. Images of the oral cavity were taken using MeMoSA®. The identification of the presence of lesion/variant and referral decision made using MeMoSA® were compared to clinical oral examination, using kappa statistics for intra-rater agreement. Sensitivity, specificity, concordance and F1 score were computed. Images were reviewed by an off-site specialist and inter-rater agreement was evaluated. Images from sequential clinical visits were compared to evaluate observable changes in the lesions.
RESULTS: Kappa values comparing MeMoSA® with clinical oral examination in detecting a lesion and referral decision was 0.604 and 0.892, respectively. Sensitivity and specificity for referral decision were 94.0% and 95.5%. Concordance and F1 score were 94.9% and 93.3%, respectively. Inter-rater agreement for a referral decision was 0.825. Progression or regression of lesions were systematically documented using MeMoSA®.
CONCLUSION: Referral decisions made through MeMoSA® is highly comparable to clinical examination demonstrating it is a reliable telemedicine tool to facilitate the identification of high-risk lesions for early management.
OBJECTIVES: This study reviewed the scope of diabetes datasets, health information ecosystems, and human resource capacity in four countries to assess whether a diabetes phenotyping algorithm (developed under a companion study) could be successfully applied.
METHODS: The capacity assessment was undertaken with four countries: Trinidad, Malaysia, Kenya, and Rwanda. Diabetes programme staff completed a checklist of available diabetes data variables and then participated in semi-structured interviews about Health Information System (HIS) ecosystem conditions, diabetes programme context, and human resource needs. Descriptive analysis was undertaken.
RESULTS: Only Malaysia collected the full set of the required diabetes data for the diabetes algorithm, although all countries did collect the required diabetes complication data. An HIS ecosystem existed in all settings, with variations in data hosting and sharing. All countries had access to HIS or ICT support, and epidemiologists or biostatisticians to support dataset preparation and algorithm application.
CONCLUSIONS: Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems' weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches.