Development and validation of a predictive model for severe caries in 12-year-olds from resource-limited regions: a cross-sectional study in China

The objective of this research was to establish and validate a predictive model for the early identification of severe caries among 12-year-olds residing in Guizhou Province, China. A cross-sectional study, including oral examination and questionnaire, was conducted involving 2,437 12-year-olds in 33 secondary schools across 11 districts (counties) in 9 cities. The participants were randomly allocated to the training and validation set in a 6:4 ratio. Severe caries was defined as DMFT ≥ 3. Crucial variables for nomogram development were determined by multivariate logistic regression. Performance evaluation of the predictive model included the area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curve, and decision curve analysis (DCA). Meanwhile, the model was further evaluated using the validation set. Among the 2,437 children, 384 (15.8%) were found to have severe caries. Significant risk factors included ‘female’ (odds ratio [OR] = 2.319; 95% confidence interval [CI]: 1.825–2.960), ‘ethnic minorities’ (OR = 1.708; 95% CI: 1.337–2.184), ‘Class III economic region’ (OR = 1.992; 95% CI: 1.487–2.690), ‘only child’ (OR = 1.993; 95% CI: 1.438–2.738), ‘self-evaluation of dental and oral conditions (very poor)’ (OR = 3.097; 95% CI: 1.724–5.419), and ‘dentist visit’ (OR = 1.746; 95% CI: 1.375–2.213). The AUCs for the training and validation sets were 0.711 (95% CI: 0.676–0.745) and 0.701 (95% CI: 0.657–0.744), indicating good discriminatory ability. The calibration curves indicated a high level of concordance between predicted risks and actual incidence rates. DCA demonstrated a net clinical benefit of the predictive model across threshold probabilities ranging from 6% to 71% in the training set and 5% to 83% in the validation set. By utilizing easily accessible variables, this model provides a simple and practical approach for the preliminary screening of severe caries among children in resource-limited areas.

Authors

Institutions

Publication Details

Journal
BMC Oral Health
Published
2026-02-23
DOI
https://doi.org/10.1186/s12903-026-07867-w
Primary Topic
Dental Health and Care Utilization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and validation of a predictive model for severe caries in 12-year-olds from resource-limited regions: a cross-sectional study in China

Yang Lin, R X Wang, Liangsa Zhang, Nanyang Zhao et al.
BMC Oral Health
Dental Health and Care Utilization
article

Development and validation of a predictive model for severe caries in 12-year-olds from resource-limited regions: a cross-sectional study in China

Yang Lin, R X Wang, Liangsa Zhang, Nanyang Zhao, Liming Chen, Arsy Huda Fathaniard, Taiming Dai, Hangfan Gao, Yifei Xu, Juanjuan Wu
article en

Abstract

The objective of this research was to establish and validate a predictive model for the early identification of severe caries among 12-year-olds residing in Guizhou Province, China. A cross-sectional study, including oral examination and questionnaire, was conducted involving 2,437 12-year-olds in 33 secondary schools across 11 districts (counties) in 9 cities. The participants were randomly allocated to the training and validation set in a 6:4 ratio. Severe caries was defined as DMFT ≥ 3. Crucial variables for nomogram development were determined by multivariate logistic regression. Performance evaluation of the predictive model included the area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curve, and decision curve analysis (DCA). Meanwhile, the model was further evaluated using the validation set. Among the 2,437 children, 384 (15.8%) were found to have severe caries. Significant risk factors included ‘female’ (odds ratio [OR] = 2.319; 95% confidence interval [CI]: 1.825–2.960), ‘ethnic minorities’ (OR = 1.708; 95% CI: 1.337–2.184), ‘Class III economic region’ (OR = 1.992; 95% CI: 1.487–2.690), ‘only child’ (OR = 1.993; 95% CI: 1.438–2.738), ‘self-evaluation of dental and oral conditions (very poor)’ (OR = 3.097; 95% CI: 1.724–5.419), and ‘dentist visit’ (OR = 1.746; 95% CI: 1.375–2.213). The AUCs for the training and validation sets were 0.711 (95% CI: 0.676–0.745) and 0.701 (95% CI: 0.657–0.744), indicating good discriminatory ability. The calibration curves indicated a high level of concordance between predicted risks and actual incidence rates. DCA demonstrated a net clinical benefit of the predictive model across threshold probabilities ranging from 6% to 71% in the training set and 5% to 83% in the validation set. By utilizing easily accessible variables, this model provides a simple and practical approach for the preliminary screening of severe caries among children in resource-limited areas.

BMC Oral Health
Guiyang Medical University (CN), Zunyi Medical University (CN), Deyang Stomatological Hospital (CN)
Openalex Percentile: Top 48%
Dental Health and Care Utilization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.