ArticleMedicina oral, patologia oral y cirugia bucal2026
A predictive model for significant periodontal disease progression: A large-scale cohort study.
Article in Medicina oral, patologia oral y cirugia bucal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- A Commentary on AI-based Prediction Models in Periodontitis Progression.Evidence-based dentistry · 2026Article
- Unsupervised phenotyping of the periodontal architecture through high-dimensional clustering of electronic health records: A multicenter study.Journal of periodontology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe progression of periodontitis is challenging to predict. This study aimed to develop and validate a machine learning model to identify patients at high risk for significant periodontal disease progression using a large dataset from electronic health records. MATERIAL AND
methodsThis retrospective cohort study included 4,117 patients with at least two comprehensive periodontal examinations separated by a minimum of 24 months. The primary outcome was significant progression, defined as a worsening of mean Clinical Attachment Level (CAL) by ≥1mm. A Random Forest Classifier was trained and validated using baseline demographic, behavioral (smoking), systemic (diabetes, high blood pressure), and periodontal (mean probing depth, mean CAL, bleeding on probing) data. Feature importance was analyzed, and a multivariable logistic regression was performed to quantify associations.
resultsOver a mean follow-up of 34.7 months, 28.0% of patients experienced significant progression. The Random Forest model demonstrated good predictive performance on the unseen test set, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.82, an accuracy of 81.6%, and a recall (sensitivity) of 79.2%. The most influential predictors were baseline mean CAL, smoking status, and age. Logistic regression confirmed these findings, showing that the odds of progression were significantly increased by higher baseline CAL (OR=2.45), current smoking (OR=1.98), a 10-year increase in age (OR=1.62), and a diagnosis of diabetes (OR=1.51).
conclusionsA machine learning model using real-world clinical data can effectively predict significant periodontal disease progression. The findings confirm that a patient's initial disease severity, smoking status, age, and diabetes are the most critical determinants of future risk, highlighting the model's potential utility in personalizing periodontal care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.