ArticleFrontiers in cellular and infection microbiology2025
Performance of salivary microbiota in detecting periodontitis using a machine learning approach.
Article in Frontiers in cellular and infection microbiology, 2025. 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
- Exploring the oral microbiome diversity and genus signatures associated with a novel non-invasive metabolic indicator: a cross-sectional study.The Saudi dental journal · 2026Article
- Integration of periodontal pathogens and inflammatory mediators in saliva as biomarkers for periodontitis.Frontiers in cellular and infection microbiology · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Altered salivary microbiota due to the progression of periodontitis may serve as a marker for simple and accurate identification of periodontitis. In this study, we examined saliva samples collected from 2,050 community-dwelling adults using 16S rRNA gene sequencing and verified the predictive performance of salivary microbiota in detecting periodontitis using a light gradient boosting machine algorithm. Five-fold stratified cross-validation was applied with 10 iterations, and the predictive performance was evaluated using the mean area under the receiver operating characteristic curve (AUC) value. In detecting periodontitis defined by number of teeth with probing depth ≥4 mm, localized (≥2 teeth), intermediate (≥4 teeth), and generalized (≥6 teeth) cases were detected with mean AUC values of 0.81 (95% confidence intervals, 0.80-0.81), 0.85 (0.84-0.86), and 0.87 (0.87-0.88), showing an increasing trend with extent. According to the Shapley additive explanation analysis,
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.