ArticleClinical oral investigations2021
External validation of a rapid, non-invasive tool for periodontitis screening in a medical care setting.
Article in Clinical oral investigations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Self-Reported Questionnaires for Detecting Periodontitis Across Multiple Cultures and Geographic Locations: A Systematic Review and Meta-Analysis.Journal of clinical periodontology · 2025Pooled it
- Diagnostic Accuracy of a Point-Of-Care aMMP-8 Test for Discriminating Periodontal Health Status in Adults: Validation Trials and Updated Meta-Analysis.Journal of clinical periodontology · 2025Pooled it
- Influence of symptomatic periodontal disease on changes in skeletal bone density during medication therapy for osteoporosis in postmenopausal women: the Japanese Osteoporosis Intervention Trial (JOINT)-04 and JOINT-05.Archives of osteoporosis · 2021Trial
- Non-invasive periodontal screening using self-reported-oral-health (SROH) questionnaire and salivary biomarkers: development and validation of machine learning models.BMC oral health · 2026Article
- A selective machine learning algorithm for severe periodontitis labeling from questionnaire data.Scientific reports · 2026Article
- Validity of a self-reported questionnaire for periodontal status in a Thai population.Journal of periodontology · 2025Article
- External Validation of Periodontal Screening Using Self-Reports in Dental Settings.Diagnostics (Basel, Switzerland) · 2025Article
- Using Dental Register Information and Questionnaire Data to Assess Periodontitis in Large Cohort Studies.Journal of clinical periodontology · 2025Article
- The impact of vaping behavior on functional changes within the subgingival microbiome.Scientific reports · 2025Article
- Combining Self-Reported Information with Radiographic Bone Loss to Screen Periodontitis: A Performance Study.Journal of clinical medicine · 2025Article
- External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study.International dental journal · 2025Article
- Attempts to Modify Periodontal Screening Models Based on a Self-Reported Oral Health Questionnaire in the Medical Care Setting.Journal of clinical periodontology · 2025Article
- Visualized analysis of hotspots and frontiers in diabetes-associated periodontal disease research: a bibliometric study.Annals of translational medicine · 2022Article
- Prevalence of Periodontal Disease among Patients at the Outpatient Clinic of Internal Medicine in an Academic Hospital in The Netherlands: A Cross-Sectional Pilot Study.Journal of clinical medicine · 2022Article
- Developing and pilot testing an oral health screening tool for diabetes care providers.BMC primary care · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesMedical professionals should advise their patients to visit a dentist if necessary. Due to the lack of time and knowledge, screening for periodontitis is often not done. To alleviate this problem, a screening model for total (own teeth/gum health, gum treatment, loose teeth, mouthwash use, and age)/severe periodontitis (gum treatment, loose teeth, tooth appearance, mouthwash use, age, and sex) in a medical care setting was developed in the Academic Center of Dentistry Amsterdam (ACTA) [1]. The purpose of the present study was to externally validate this tool in an outpatient medical setting. MATERIALS AND
methodsPatients were requited in an outpatient medical setting as the validation cohort. The self-reported oral health questionnaire was conducted, demographic data were collected, and periodontal examination was performed. Algorithm discrimination was expressed as the area under the receiver operating characteristic curve (AUROCC). Sensitivity, specificity, and positive and negative predictive values were calculated. Calibration plots were made.
resultsFor predicting total periodontitis, the AUROCC was 0.59 with a sensitivity of 49% and specificity of 68%. The PPV was 57% and the NPV scored 55%. For predicting severe periodontitis, the AUROCC was 0.73 with a sensitivity of 71% and specificity of 63%. The PPV was 39% and the NPV 87%.
conclusionsThe performance of the algorithm for severe periodontitis is found to be sufficient in the current medical study population. Further external validation of periodontitis algorithms in non-dental school populations is recommended. CLINICAL RELEVANCE: Because general physicians are obligated to screen patients for periodontitis, it is our general goal that they can use a prediction model in medical settings without an oral examination.
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