The World Health Organisation Resolution on Oral Health, the WHO Global Strategy and Action Plan on Oral Health 2023–2030, and the Bangkok Declaration of 2024 together mark an unprecedented convergence of global policy commitment, professional responsibility, and opportunity for oral health system reform. These instruments position oral health within the broader agendas of noncommunicable disease, primary health care, Sustainable Development Goal 3, and universal health coverage, and call for oral health systems that are integrated, prevention-oriented, equitable, accountable, and environmentally sustainable. The principal challenge now is implementation: translating these commitments into sustained action across diverse health system contexts. This review argues that implementation science provides the methodological and strategic foundation required to translate global oral health policy into sustained action. By distinguishing between evidence-based interventions and the strategies needed to adopt, adapt, scale, sustain, and, where necessary, de-implement them, implementation science can bridge the persistent evidence–practice gap in dentistry. It offers tools for strengthening governance, integrating oral health into primary health care, reallocating resources away from low-value care, embedding implementation outcomes in monitoring systems, accelerating translation through hybrid effectiveness–implementation designs, and ensuring that equity is measured rather than merely asserted. Advancing universal health coverage for oral health by 2030, requires context-sensitive, evidence-informed, and accountable execution. Implementation science should therefore be regarded as a core discipline for transforming global oral health policy into practice.
Objectives Periodontitis (PD) is a prevalent chronic inflammatory disease with substantial global burden, yet its genetic architecture remains incompletely understood. Although smoking is a major risk factor, many individuals develop PD without smoking exposure, suggesting biologically distinct pathogenic pathways. This study aimed to disentangle the genetic architecture underlying smoking-related and smoking-independent PD and to characterize their distinct biological mechanisms. Methods We applied a genome-wide association study-by-subtraction framework to summary statistics from 2669,029 individuals to partition PD into smoking-related and smoking-independent genetic components. Integrative analyses included Bayesian fine-mapping, transcriptome-wide and proteome-wide association analyses, colocalization, neuroimaging-informed association analyses, and integration with gingival single-cell transcriptomic data. Results The smoking-related component indicated extensive polygenic architecture, identifying 287 independent loci and widespread molecular associations. In contrast, the smoking-independent component showed markedly fewer signals, including one genome-wide significant locus and a limited number of candidate genes. FOXO3 and MST1 were consistently prioritized in the smoking-related component, implicating oxidative stress, immune regulation, and neurobehavioural pathways. Conversely, AGER emerged as the principal candidate gene in the smoking-independent component, suggesting distinct inflammatory mechanisms. Neuroimaging analyses further linked smoking-related genetic liability to brain regions involved in reward and behavioural regulation. Conclusions Our findings highlight distinct smoking-related and smoking-independent genetic components of PD that may be obscured in conventional genetic analyses. These findings provide new insights into the heterogeneous genetic architecture of PD, prioritize candidate genes for future functional investigation, and may inform future genetically informed risk assessment, precision prevention, and host-modulating therapeutic strategies.
Introduction and aims To evaluate oral health knowledge, attitudes, and practices among pregnant women in Fuzhou, China, and test the hypothesis that receiving obstetrician advice is independently associated with higher prenatal dental attendance. Methods A cross-sectional survey of pregnant women was conducted using consecutive trimester-stratified quota sampling at the Department of Obstetrics of a tertiary public hospital in Fuzhou, China. Questionnaire reliability and validity were assessed. Gestational age was determined via last menstrual period and confirmed by early pregnancy ultrasound scan (<14 weeks). Factors associated with dental visits were evaluated using multivariable logistic regression after collapsing sparse variable categories to ensure model stability (Study Registration Filing No.: MR-35-25-062116). Results Of 500 approached women, 452 were analysed (96.0% response rate; 18 excluded for failing attention checks). Only 13.1% (59/452) reported a prenatal dental visit, with 91.5% (54/59) driven by acute toothache. Mean knowledge, attitude, and practice scores (4.29 ± 1.25, 24.87 ± 2.09, and 2.92 ± 1.73) highlighted a significant attitude–practice gap. Lacking obstetrician advice was the primary barrier for nonattenders (53.2%). Multivariable logistic regression revealed obstetrician advice was the strongest independent factor associated with attendance (adjusted odds ratio [aOR] 6.32; 95% CI 3.38-11.84; P < .001). Late pregnancy (aOR 3.58; P = .002), higher income (aOR 4.23; P = .029), and recent prepregnancy dental checks (aOR 2.10; P = .018) were also independently associated with higher utilization. Conclusion Dental service utilization during pregnancy is low, predominantly symptom-driven, and marked by an attitude–practice gap. Receiving obstetrician advice is strongly associated with attendance. Establishing institutionalized ‘Ask-Advise-Refer’ pathways within routine antenatal care is a practical strategy to optimize maternal oral healthcare utilization. Clinical relevance Integrating brief oral health counselling and streamlined cross-referral pathways into standard antenatal visits could significantly reduce missed opportunities for preventive dental care during pregnancy.
Objective To explore the feasibility of developing an interpretable machine learning (ML) model that integrates clinical variables and quantitative radiomics features to predict 12-month outcomes of nonsurgical root canal retreatment (NSreRCT). Materials and Methods In this retrospective case-control study, 200 teeth (100 failed and 100 successful NSreRCT) were included, matched by age, tooth type, and periapical status. For each case, 30 preoperative clinical variables and periapical radiographs were collected. Radiomics features were extracted from the core and boundary regions of periapical lesions. Seven ML classifiers were trained and validated using the 5/10 most informative radiomics and/or clinical variables, identified through supervised feature selection nested within stratified 5-fold cross-validation. Within each training partition, 448 candidate pipelines were ranked using an internal hold-out evaluation. Model performance was assessed using accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, Area Under Precision-Recall Curve, mean Area Under Receiver Operating Characteristic Curve (AUC) and stacked AUC. Feature importance was evaluated using SHapley Additive exPlanations analysis. Results The Rad10AndCli10 model incorporating the top 10 radiomics features and top 10 clinical variables achieved the highest performance (stacked AUC: 0.902), outperforming the Rad5AndCli5 model (stacked AUC: 0.896) and Rad10 model (AUC: 0.866) or Cli10 model (AUC: 0.750). The Rad10AndCli10 model demonstrated stable performance across all 5 folds (AUC range: 0.862-0.945). Radiomics features from lesion boundaries contributed most to predictions, and higher lesion heterogeneity was associated with a greater risk of treatment failure. Clinical predictors including deepest periodontal probing depth and size of periapical lesion size, were consistently selected. Conclusion ML models combining radiomics features and clinical variables improved prediction of NSreRCT outcomes compared with models using either data source alone. Radiomics features, particularly those derived from periapical lesion boundaries, contributed substantially to prediction, and greater lesion heterogeneity was associated with a higher risk of NSreRCT failure.
