ArticleNPJ digital medicine2025
Predictive modeling for step II therapy response in periodontitis - model development and validation.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- Machine learning prediction of CKD progression in hyperglycemic elderly adults: a prospective community cohort study.Renal failure · 2026Article
- Prediction and risk factor analysis of dental implant failure using Cox regression and machine learning.Journal of periodontology · 2026Article
- Role of artificial intelligence in determining nutritional risk factors among post-periodontal surgical patients. A scoping review.Frontiers in oral health · 2026Review
- Molecular cytokine-dependent mechanisms of periodontitis pathogenesis and the potential for pharmacological IL-1 modulation in disease treatment.Frontiers in dental medicine · 2026Review
- PerioTwin-DeltaCRED: a validation-aware physiological digital twin for longitudinal periodontal treatment response prediction.Frontiers in physiology · 2026Article
- A Clinical Review of the Connections Between Diabetes Mellitus, Periodontal Disease, and Cardiovascular Pathologies.Biomedicines · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Steps I and II periodontal therapy is the first-line treatment for periodontal disease, but has varying success. This study aimed to develop machine learning models to predict changes in periodontal probing depth (PPD) after step II therapy using patient-, tooth-, and site-specific clinical covariates. Models accurately predicted that healthy sites stay healthy, but performed suboptimally for diseased sites. Tuning improved performance, with PPD, tooth-site, and tooth-type identified as key predictors. Pocket closure was predicted with fair accuracy, with baseline PPD as the most relevant covariate. Models predicted improving pockets well but underperformed for non-responding sites, with antibiotic treatment and tooth type being the most influential features. While predictive performance for step II periodontal therapy based on routine clinical data remains limited, models can stratify periodontal sites into meaningful categories and estimate the probability of pocket improvement. They provide a foundation for site-specific outcome prediction and may support patient communication and expectations.
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.