Dataset Information

Fluorides and Other Preventive Strategies for Tooth Decay.

ABSTRACT: We focus on scalable public health interventions that prevent and delay the development of caries and enhance resistance to dental caries lesions. These interventions should occur throughout the life cycle, and need to be age appropriate. Mitigating disease transmission and enhancing resistance are achieved through use of various fluorides, sugar substitutes, mechanical barriers such as pit-and-fissure sealants, and antimicrobials. A key aspect is counseling and other behavioral interventions that are designed to promote use of disease transmission-inhibiting and tooth resistance-enhancing agents. Advocacy for public water fluoridation and sugar taxes is an appropriate dental public health activity.

SUBMITTER: Horst JA

PROVIDER: S-EPMC5830181 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Fluorides and Other Preventive Strategies for Tooth Decay.

Horst Jeremy A JA Tanzer Jason M JM Milgrom Peter M PM

Dental clinics of North America 20180401 2

We focus on scalable public health interventions that prevent and delay the development of caries and enhance resistance to dental caries lesions. These interventions should occur throughout the life cycle, and need to be age appropriate. Mitigating disease transmission and enhancing resistance are achieved through use of various fluorides, sugar substitutes, mechanical barriers such as pit-and-fissure sealants, and antimicrobials. A key aspect is counseling and other behavioral interventions th ...[more]

PMID: 29478454

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Project description:Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients' age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.

Dataset Information

Fluorides and Other Preventive Strategies for Tooth Decay.

Publications

Fluorides and Other Preventive Strategies for Tooth Decay.

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