A systematic review of caries risk in children <6 years of age
Bibliographic record
Abstract
BACKGROUND: For caries risk assessment (CRA) tools for young children to be evidence-based, it is important to systematically review the literature to identify factors associated with the onset of early childhood caries (ECC). AIM: This updated systematic review aimed to identify current evidence on caries risk in young children. DESIGN: A comprehensive and systematic literature search of relevant databases was conducted to update a previous systematic review and identify risk factors associated with ECC. Potential risk factors were identified based on strength of association using odds ratios, hazard ratios, relative risk, etc. GRADE was used for rating quality evidence through consensus. RESULTS: Twenty-two studies met inclusion criteria for the search from mid-2017 to 2021. Twenty-five publications from the prior systematic review, from 1997 to mid-2017, were also included. Several socioeconomic, behavioral, and clinical variables were identified as ECC risk factors. Factors included the following: age, socioeconomic status, frequency of and supervised toothbrushing, fluoride exposure, breast- and bottle-feeding, feeding habits, absence of a dental home, past caries experience, active non-cavitated lesions, visible plaque, enamel defects, and microbiome. CONCLUSION: This study provides updated evidence of risk factors for ECC that could be included in CRA tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".