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Systematic Approach to Identify Novel Antimicrobial and Antibiofilm Molecules from Plants' Extracts and Fractions to Prevent Dental Caries
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A Predictive Model for Root Caries Incidence.

André V Ritter1, John S Preisser, Chaitanya P Puranik

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|May 11, 2016
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Summary
This summary is machine-generated.

Identifying root caries (RC) risk indicators in adults is crucial. The best predictors for new root caries include more root surfaces at risk and a higher RC index, alongside race and smoking status.

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Area of Science:

  • Dental Research
  • Epidemiology
  • Public Health

Background:

  • Root caries (RC) poses a significant threat to oral health in adults.
  • Predicting RC incidence is essential for targeted preventive strategies.
  • Previous models have not fully captured the complexity of RC risk factors.

Purpose of the Study:

  • To identify the optimal set of risk indicators for predicting root caries incidence in caries-active adults.
  • To compare the predictive performance of different logistic regression models.
  • To evaluate the impact of ancillary variables on RC prediction.

Main Methods:

  • Utilized data from the Xylitol for Adult Caries Trial (X-ACT) placebo-control group (n=155).
  • Compared five logistic regression models, assessing baseline and ancillary variables (smoking, diet, RPD use, etc.).
  • Conducted a sensitivity analysis including treatment data (n=301) to validate predictive models.

Main Results:

  • Forty-nine percent of control participants developed incident RC.
  • The best predictive model included follow-up years, root surfaces at risk, RC index, gender, race, age, and smoking.
  • Increased root surfaces at risk and RC index were key predictors; white race and nonsmoking were significant but not statistically.
  • Ancillary variables like diet, RPD use, and socioeconomic factors did not improve prediction.

Conclusions:

  • A robust model for predicting root caries incidence in adults has been identified.
  • Key predictors include baseline root surface status and RC index, with race and smoking also playing roles.
  • Further research can refine these indicators for enhanced clinical risk assessment and prevention.