Evidence map›Paper›PMID 31710817›Full record

ArticleJDR clinical and translational research2020

Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.

Y Wang, R D Hays, M Marcus, C A Maida, J Shen, D Xiong, I D Coulter, S Y Lee, V W Spolsky, J J Crall and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JDR clinical and translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  14. Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023
    Article
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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

11 authors.

Y WangDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA.ORCID 0000-0002-4822-6514
R D HaysDepartment of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, CA, USA.ORCID 0000-0001-6697-907X
M MarcusDivision of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.ORCID 0000-0002-4351-2060
C A MaidaDivision of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.
J ShenDivision of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.
D XiongDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA.
I D CoulterDivision of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.
S Y LeeDivision of Constitutive & Regenerative Sciences, Section of Restorative Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.
V W SpolskyDivision of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.ORCID 0000-0002-0643-2475
J J CrallDivision of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA.
H LiuDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA.

Funding

Patient-Reported Oral Health Outcomes Measurement Information SystemU01DE022648 · NIDCR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LIU, HONGHU · 2013 to 2016
$2.7M
NIDCR NIH HHS U01 DE022648
6 · The paper itself

Abstract

objectivesEvaluating children's oral health status and treatment needs is challenging. We aim to build oral health assessment toolkits to predict Children's Oral Health Status Index (COHSI) score and referral for treatment needs (RFTN) of oral health. Parent and Child toolkits consist of short-form survey items (12 for children and 8 for parents) with and without children's demographic information (7 questions) to predict the child's oral health status and need for treatment.

methodsData were collected from 12 dental practices in Los Angeles County from 2015 to 2016. We predicted COHSI score and RFTN using random Bootstrap samples with manually introduced Gaussian noise together with machine learning algorithms, such as Extreme Gradient Boosting and Naive Bayesian algorithms (using R). The toolkits predicted the probability of treatment needs and the COHSI score with percentile (ranking). The performance of the toolkits was evaluated internally and externally by residual mean square error (RMSE), correlation, sensitivity and specificity.

resultsThe toolkits were developed based on survey responses from 545 families with children aged 2 to 17 y. The sensitivity and specificity for predicting RFTN were 93% and 49% respectively with the external data. The correlation(s) between predicted and clinically determined COHSI was 0.88 (and 0.91 for its percentile). The RMSEs of the COHSI toolkit were 4.2 for COHSI (and 1.3 for its percentile).

conclusionsSurvey responses from children and their parents/guardians are predictive for clinical outcomes. The toolkits can be used by oral health programs at baseline among school populations. The toolkits can also be used to quantify differences between pre- and post-dental care program implementation. The toolkits' predicted oral health scores can be used to stratify samples in oral health research. KNOWLEDGE TRANSFER STATEMENT: This study creates the oral health toolkits that combine self- and proxy- reported short forms with children's demographic characteristics to predict children's oral health and treatment needs using Machine Learning algorithms. The toolkits can be used by oral health programs at baseline among school populations to quantify differences between pre and post dental care program implementation. The toolkits can also be used to stratify samples according to the treatment needs and oral health status.

Indexed as

Machine LearningOral HealthAdolescentAlgorithmsBayes TheoremChildChild, PreschoolHumansSurveys and Questionnaireshealth services researchpatient reported outcome measuresproxypsychometricsself reportsurveys and questionnaires

Identifiers

PMID31710817
PMCPMC7298887

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.