ArticleJournal of clinical periodontology2025
Attempts to Modify Periodontal Screening Models Based on a Self-Reported Oral Health Questionnaire in the Medical Care Setting.
Article in Journal of clinical periodontology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning-based analysis of oral rinse samples to identify candidate proteomic signatures for severe periodontitis: a pilot study.Scientific reports · 2026Article
- A selective machine learning algorithm for severe periodontitis labeling from questionnaire data.Scientific reports · 2026Article
- External Validation of Periodontal Screening Using Self-Reports in Dental Settings.Diagnostics (Basel, Switzerland) · 2025Article
- Combining Self-Reported Information with Radiographic Bone Loss to Screen Periodontitis: A Performance Study.Journal of clinical medicine · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
aimPeriodontal disease (PD) screening models based on a self-reported questionnaire were previously established and externally validated. The aim of the present study is to explore whether the screening models could be modified to improve prediction performance; this methodology is called 'updating'.
methodsUpdating the models for 'total' and 'severe' PD was performed using two datasets. One dataset from a previous study (n = 155) was used to explore the updating, and a second (n = 187, built for the current study) was used to validate whether updating improved performance. Updating was based on different statistical approaches, including model recalibration and revision. Discrimination and calibration were assessed after updating.
resultsFor 'total' PD, the update based on model revision improved its performance. However, still low AUCs were found: 0.64 (0.56-0.73) and 0.61 (0.53-0.69) with corresponding O:E ratios 1.00 (0.80-1.23) and 0.92 (0.75-1.13) in the update and validation cohorts, respectively. For 'severe' PD, performance of the original model without update performed still the best; AUCs were 0.72 (0.61-0.83) and 0.75 (0.66-0.84) in the update and validation cohorts, respectively, with corresponding O:E ratios 0.60 (0.38-0.84) and 0.62 (0.42-0.87).
conclusionsThe updating methodology did not further improve the performance of the original 'severe' PD screening model; it performed satisfactorily in the medical care setting. Despite updating attempts, the screening model for 'total' PD remained sub-optimal. Screening for 'severe' PD can now be implemented in the medical care setting.
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