A systematic review to inform the development of a Canadian caries risk assessment tool for use by primary healthcare providers
Bibliographic record
Abstract
BACKGROUND: Caries risk assessment (CRA) tools may assist in identifying children at risk of early childhood caries. AIM: To complete a systematic review of CRA and develop a Canadian CRA tool for preschool children for use in non-dental clinical settings. DESIGN: Systematic searches of relevant databases were conducted. Potential variables were based on strength of associations (odd ratios, relative risk, hazard ratios, etc), frequency of occurrence, and existing CRA tools. Quality of the evidence assessments were performed by at least two review teams through consensus following GRADE. RESULTS: Overall, 25 publications met the inclusion criteria, all prospective in design. Based on this review, variables to be considered when developing a new CRA tool for use with preschool children are as follows: age, socioeconomic status, family toothbrushing habits, fluoride exposure, infant feeding practices, dietary habits/behaviours, dental home, caries experience, visible plaque, and enamel defects. The environmental scan identified 22 CRA tools suggesting other additional variables to consider including in a CRA tool, including special healthcare needs, enamel defects, and dental attendance. CONCLUSIONS: This review informed the development of a Canadian CRA tool for use by primary healthcare professionals, which may improve access to oral health assessments and increase interprofessional collaboration.
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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 | 0.029 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".