Evidence map›Paper›PMID 41830787›Full record

ArticleInternational dental journal2026

Machine Learning Models for Identifying Dental Pain in Adolescents.

Luiz Alexandre Chisini, Luana Carla Salvi, Francine Dos Santos Costa, Fausto Medeiros Mendes, Luiz Felipe da Silva Pinto, Maximiliano Sérgio Cenci, Tatiana Pereira-Cenci, Flávio Fernando Demarco

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Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Luiz Alexandre ChisiniGraduate Program in Dentistry, Federal University of Pelotas, Rio Grande do Sul, Pelotas, Brazil; Radboud University Medical Center, Department of Dentistry, Nijmegen, The Netherlands. Electronic address: luiz.a.Chisini@radboudumc.nl.
Luana Carla SalviRadboud University Medical Center, Department of Dentistry, Nijmegen, The Netherlands; Graduate Program in Biotechnology, Technological Development Center, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil.
Francine Dos Santos CostaGraduate Program in Biotechnology, Technological Development Center, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil.
Fausto Medeiros MendesDepartment of Pediatric Dentistry, University of São Paulo, São Paulo, Brazil.
Luiz Felipe da Silva PintoPost-Graduation Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Maximiliano Sérgio CenciRadboud University Medical Center, Department of Dentistry, Nijmegen, The Netherlands.
Tatiana Pereira-CenciRadboud University Medical Center, Department of Dentistry, Nijmegen, The Netherlands.
Flávio Fernando DemarcoGraduate Program in Dentistry, Federal University of Pelotas, Rio Grande do Sul, Pelotas, Brazil; Radboud University Medical Center, Department of Dentistry, Nijmegen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis study aimed to identify dental pain using machine learning (ML) algorithms in Brazilian adolescents for public health screening purposes.

methodsData from 2 cross-sectional waves of the Brazilian National Survey of School Health (PeNSE) in 2015 and 2019 were used (schoolchildren aged 11 to 18). The outcome was dental pain in the last 6 months. Co-variables were 53 variables, including demographic, socioeconomic, and behavioral characteristics. The 2015 dataset was split (80:20) into training and test sets, while the 2019 dataset was used as a temporal external validation set. Nine ML models were evaluated.

resultsA total of 259,833 adolescents (97.0% of the sample) were included. Dental pain prevalence was 19.5% (95% CI, 19.2-19.8). Extra Trees (ET) was the model with the best metrics in the test and external validation sets. ET showed an AUC = 0.64 (95% CI, 0.63-0.65) and a Recall = 0.57 in the test, and AUC = 0.62 (95% CI, 0.62-0.63) and Recall = 0.57 in the external test, indicating a modest ability to discriminate adolescents with dental pain and to identify approximately 57 out of 100 affected individuals. Fairness estimations show lower accuracy for males, but a higher recall for this group. The model shows a higher accuracy for white adolescents but a lower recall for this group. The Shapley values showed that sex, alcohol consumption, and family violence were the most important variables in the algorithm's identification process.

conclusionThis study shows the potential of ML to identify dental pain in adolescents. Modest predictive performance and fairness limitations highlight the need for improvements before widespread adoption.

Indexed as

Machine LearningToothacheAdolescentBrazilChildClassification AlgorithmsCross-Sectional StudiesFemaleHumansMalePrediction AlgorithmsPredictive Learning ModelsPrevalenceDental painDiagnostic modelsOral diseases

Identifiers

PMID41830787
PMCPMC12996636

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.