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Abstract
Background
Knowledge of molar incisor hypomineralization (MIH) has relevance for paediatric dentists.
Aim
To assess final‐year German dental students’ knowledge, attitudes, and beliefs regarding MIH.
Materials and methods
A previously validated questionnaire was posted to the 31 German dental schools. Demographic covariates as well as knowledge regarding
diagnosis and prevalence, and attitudes and beliefs around aetiology and management were collected.
Results
Twenty‐two (71%) dental schools responded and a total of 877 students participated. Most (97%) were familiar with MIH and 88% were aware of the diagnostic criteria for MIH; however, only 42% knew how to implement them. One‐third were able to identify MIH and 16% reported diagnostic confidence when doing so; 90% assumed the MIH prevalence to be <10%. Two‐thirds of the respondents implicated genetic components as the main aetiological factor of MIH. Resin composite (60%) and preformed metal crowns (46%) were the dental materials most often suggested for restorative management. Almost all (98%) respondents were interested in receiving more clinical training.
Conclusion
German students were familiar with MIH; however, they reported low levels of knowledge and confidence regarding its prevalence and diagnosis. Standardized nationwide, up‐to‐date curricula should be implemented to educate future dentists in Germany.
Aim: To provide recommendations for dental clinicians for the management of dental caries in older adults with special emphasis on root caries lesions. Methods: A consensus workshop followed by a Delphi consensus process were conducted with an expert panel nominated by ORCA, EFCD, and DGZ boards. Based on a systematic review of the literature, as well as non-systematic literature search, recommendations for clinicians were developed and consented in a two-stage Delphi process. Results: Demographic and epidemiologic changes will significantly increase the need of management of older adults and root caries in the future. Ageing is associated with a decline of intrinsic capacities and an increased risk of general diseases. As oral and systemic health are linked, bidirectional consequences of diseases and interventions need to be considered. Caries prevention and treatment in older adults must respond to the patient’s individual abilities for self-care and cooperation and often involves the support of caregivers. Systemic interventions may involve dietary counselling, oral hygiene instruction, the use of fluoridated toothpastes, and the stimulation of salivary flow. Local interventions to manage root lesions may comprise local biofilm control, application of highly fluoridated toothpastes or varnishes as well as antimicrobial agents. Restorative treatment is often compromised by the accessibility of such root caries lesions as well as the ability of the senior patient to cooperate. If optimum restorative treatment is impossible or inappropriate, long-term stabilization, e.g., by using glass-ionomer cements, and palliative treatments that aim to maintain oral function as long and as well as possible may be the treatment of choice for the individual.
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.