Evaluation of a Digital Decision Support System to Integrate Type 2 Diabetes Mellitus and Periodontitis Care: Case-Vignette Study in Simulated Environments

Kalmus, Olivier; Smits, Kirsten; Seitz, Max; Haux, Christian; Robra, Bernt-Peter; Listl, Stefan

doi:10.2196/46381

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…smoking cessation) and that patients with diagnosed systemic conditions, like diabetes, may need more intense supporting care. Risk assessment tools provide no added value as they do not overcome the dental provider barriers of lack of training in, and insufficient knowledge about, smoking cessation interventions 62 and they do not provide the information evidently necessary to prompt a dental practitioner to pursue the diabetes issue even when they are given strong cues that diabetes might be an issue of concern 63 . Finally, interventions comprising regular/routine scaling and root planing are already widely implemented despite their unknown effect on the prevention of periodontitis 64 .…”

Section: Periodontal Risk Prediction: Unknown Impactsmentioning

confidence: 99%

Contesting the conventional wisdom of periodontal risk assessment

Raittio,

Lopez,

Baelum

2024

Comm Dent Oral Epid

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Over the years, several reviews of periodontal risk assessment tools have been published. However, major misunderstandings still prevail in repeated attempts to use these tools for prognostic risk prediction. Here we review the principles of risk prediction and discuss the value and the challenges of using prediction models in periodontology. Most periodontal risk prediction models have not been properly developed according to guidance given for the risk prediction model development. This shortcoming has led to several problems, including the creation of arbitrary risk scores. These scores are often labelled as ‘high risk’ without explicit boundaries or thresholds for the underlying continuous risk estimates of patient‐important outcomes. Moreover, it is apparent that prediction models are often misinterpreted as causal models by clinicians and researchers although they cannot be used as such. Additional challenges like the critical assessment of transportability and applicability of these prediction models, as well as their impact on clinical practice and patient outcomes, are not considered in the literature. Nevertheless, these instruments are promoted with claims regarding their ability to deliver more individualized and precise periodontitis treatment and prevention, purportedly resulting in improved patient outcomes. However, people with or without periodontitis deserve proper information about their risk of developing patient‐important outcomes such as tooth loss or pain. The primary objective of disseminating such information should not be to emphasize assumed treatment efficacy, hype individualization of care, or promote business interests. Instead, the focus should be on providing individuals with locally validated and regularly updated predictions of specific risks based on readily accessible and valid key predictors (e.g. age and smoking).

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Section: Periodontal Risk Prediction: Unknown Impactsmentioning

confidence: 99%