Evidence map›Paper›PMID 25731155›Full record

ArticleJournal of dentistry2015

Baseline caries risk assessment as a predictor of caries incidence.

Benjamin W Chaffee, Jing Cheng, John D B Featherstone

Open access · greenAbstract read
In one paragraph

Article in Journal of dentistry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 5% of its field
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

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.

  1. Pooled it
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  7. [Machine learning-based prediction model for caries in the first molars of 9-year-old children in Suzhou].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2025
    Article
  8. Caries lesions progression in adults: A prospective 2-year cohort study.Community dentistry and oral epidemiology · 2025
    Article
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  20. Oral health indices predict individualised recall interval.Clinical and experimental dental research · 2020
    Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Benjamin W ChaffeeDepartment of Preventive and Restorative Dental Sciences, University of California San Francisco School of Dentistry, 707 Parnassus Avenue, San Francisco, CA 94143-0758, USA. Electronic address: benjamin.chaffee@ucsf.edu.
Jing ChengDepartment of Preventive and Restorative Dental Sciences, University of California San Francisco School of Dentistry, 707 Parnassus Avenue, San Francisco, CA 94143-0758, USA. Electronic address: jing.cheng@uscf.edu.
John D B FeatherstoneDepartment of Preventive and Restorative Dental Sciences, University of California San Francisco School of Dentistry, 707 Parnassus Avenue, San Francisco, CA 94143-0758, USA. Electronic address: jdbf@ucsf.edu.
University of California, San Francisco · US

Funding

Clinical and Transtional Science InstituteKL2TR000143 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRANDIS, JENNIFER RUBIN · 2012 to 2015
$11.9M
NCATS NIH HHS KL2 TR000143NCATS NIH HHS KL2TR000143
6 · The paper itself

Abstract

unlabelledFew studies have evaluated clinical outcomes following caries risk assessment in large datasets that reflect risk assessments performed during routine practice.

objectivesFrom clinical records, compare 18-month caries incidence according to baseline caries risk designation.

methodsFor this retrospective cohort study, data were collected from electronic records of non-edentulous adult patients who completed an oral examination and caries risk assessment (CRA) at a university instructional clinic from 2007 to 2012 (N=18,004 baseline patients). The primary outcome was the number of new decayed/restored teeth from the initial CRA to the ensuing oral examination, through June 30, 2013 (N=4468 patients with follow-up). We obtained doubly-robust estimates for 18-month caries increment by baseline CRA category (low, moderate, high, extreme), adjusted for patient characteristics (age, sex, payer type, race/ethnicity, number of teeth), provider type, and calendar year.

resultsAdjusted mean decayed, restored tooth (DFT) increment from baseline to follow-up was greater with each rising category of baseline caries risk, from low (0.94), moderate (1.26), high (1.79), to extreme (3.26). The percentage of patients with any newly affected teeth (DFT increment>0) was similar among low-risk and moderate-risk patients (cumulative incidence ratio, RR: 1.01; 95% confidence interval, CI: 0.83, 1.23), but was increased relative to low-risk patients among high-risk (RR: 1.28; 95% CI: 1.10, 1.52), and extreme-risk patients (RR: 1.52; 95% CI: 1.23, 1.87).

conclusionsThese results lend evidence that baseline caries risk predicts future caries in this setting, supporting the use of caries risk assessment to identify candidate patients for more intensive preventive therapy. CLINICAL SIGNIFICANCE: Identification of patients at greater risk for future caries helps clinicians to plan appropriate personalized care. In this study, a multifactorial approach to caries risk assessment effectively stratified patients into groups of higher or lower caries propensity. Dentists can apply risk assessment in practice antecedent to patient-tailored caries management.

Indexed as

AdolescentAdultAgedAged, 80 and overCohort StudiesDental CareDental CariesElectronic Health RecordsFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedNeoplasmsPreventive DentistryCaries managementCaries risk assessmentDental cariesEpidemiologyLongitudinal studies

Identifiers

PMID25731155
PMCPMC4417378
OpenAlexW1990781351

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.