English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The establishment and sustainment of a high state of dental readiness in the Canadian Armed Forces (CAF) are the primary missions of the Royal Canadian Dental Corps. The objective of this study was to develop a risk prediction tool to estimate dental readiness in active CAF personnel. Materials and Methods The prediction model was developed to predict the classification of non-deployable (yes/no) within 12 months (primary) and 18 months (secondary) using both dental history data (including dental attendance, restorations, root canals, and third molar status) and demographic information. Two cohorts were used for development: a recruit cohort who enrolled between April 2016 and March 2017 and a longer-serving member (LSM) cohort who had their recall dental exam between May 2014 and October 2014. Each group was followed until April 26, 2018. Elastic net logistic regression models were used to create the models. Model performance was evaluated using area under the curve, F1, and the Brier score. Results The recruit cohort included 2,828 individuals and the LSM cohort included 2,398 individuals. Overall, the classification of non-deployable occurred in 5.1% of the study population within 12 months and 9.6% of the population within 18 months. The models predicted the outcome with an area under the receiver operating curve of 0.77 in recruits and 0.70 in LSMs. Conclusion The prediction model shows potential but its performance and usability could be further improved through the consistent collection of high quality, discretely entered, epidemiological data following standardized diagnostic terminology and coding. A recalibrated and automated version of this model could assist in decision making, resource allocation, and the enhancement of military dental readiness.

Development of a Risk Prediction Model for Assessing Dental Readiness in the Canadian Armed Forces