Child temperament and early childhood caries: is there a link?
Bibliographic record
Abstract
DATA SOURCES: A systematic search was conducted across multiple databases (MEDLINE via PubMed, EMBASE, Scopus, LILACS, Web of Science, and EBSCO) up to January 2023. STUDY SELECTION: Any case-control, cohort, or cross-sectional study which assessed child temperament and early childhood caries (ECC) in children aged six years or younger were included. Literature reviews, studies with insufficient data, non-English publications, and those focusing on older children or adults were excluded. DATA EXTRACTION AND SYNTHESIS: Data extraction was conducted independently by two authors, with a third author resolving any disagreements. Risk of bias was assessed using the Newcastle-Ottawa assessment scale (case-control and cohort studies) and the Appraisal tool for Cross-Sectional Studies (cross-sectional studies). The quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Statistical analysis to evaluate heterogeneity included the chi-square test and the I-square index. RESULTS: A total 5072 studies resulted in the inclusion of 15 studies, encompassing data from 6,667 participants. Seven studies were of high quality and eight, moderate. Meta-analyses of seven studies revealed a significant association between certain temperament traits (e.g., higher levels of emotionality and lower levels of sociability) and ECC. In particular, difficult temperament was associated with ECC (OR 2.63 95%CI: 1.37-5.04) CONCLUSIONS: The study concluded that child temperament is a significant factor in the risk of developing ECC. Specifically, children with higher emotionality and lower sociability are at greater risk. Interventions targeting child temperament through child behaviour and parental management strategies may be effective in reducing ECC.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".