Utilization of caries risk assessment tools within the underserved population: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Caries Risk Assessment (CRA) tools can be utilised to assess caries risk levels within underserved individuals to provide risk-based caries management. With no previous review mapping the evidence of CRA tools in underserved populations, a scoping review was conducted to provide a comprehensive view of the current literature and the utilisation of CRA tools in underserved populations. The main objectives of this review are as follows: (1) to comprehensively review CRA tools utilised, and (2) to highlight the important findings indicating the oral health status of underserved population subgroups. METHODS: A systematic search was performed using MEDLINE, EMBASE, Scopus, Google Scholar, and Dissertations & Theses Global (ProQuest). All relevant English-language papers published between January 2004 to June 2024 were identified. Retrieved references were imported and underwent 2-stage screening. The type of CRA tool was extracted as the primary outcome and oral health status of underserved subgroups were extracted as the secondary outcome. RESULTS: A total of 26 studies and nine different CRA tools were identified. Included studies examined caries risk in low-income families, people with disabilities, Indigenous peoples, refugees, veterans, and rural communities. Most studies indicated moderate to high caries risk and significant unmet oral health needs in underserved populations. CONCLUSIONS: The underserved populations experience elevated caries risk and poor oral health status that require the attention of policymakers and practitioners. Significant heterogeneity across the utilised CRA tools was identified. Future research focusing on developing a standardised and appropriately validated CRA tool that can be utilised is necessary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".