Microbiome in Early Childhood Caries: Caries Severity-Dependent Insights
Bibliographic record
Abstract
INTRODUCTION: Children with early childhood caries (ECC) show different caries severities and susceptibility in different tooth types and location in the oral cavity. The study aimed to investigate differences in the oral microbiome in ECC subjects stratified according to the severity of caries and between more and less caries-prone teeth within the same subjects. METHODS: Supragingival plaque from the upper and lower anterior regions in the oral cavity of subjects were collected in 3 groups of increasing caries severity: G1 - molar (M) caries only; G2 - molar and upper anterior (UA) caries; and G3 - M + UA + lower anterior (LA) caries followed by microbiome analysis. RESULTS: Alpha-diversity analyses showed inter- but no intra-individual statistically significant differences between the UA and LA (p < 0.001, LA > UA) and a significant difference between the microbiome of the three caries groups (p < 0.001). There were significant beta-diversity differences between G1 and G2 (p < 0.05) and in the composition and diversity among the three groups (p < 0.001). Actinomyces, Saccharibacteria_genera_inserta_sedis, and Eikenella had increased differential abundance in G1 versus G3 and Fusobacterium was less abundant in G2 compared to the other groups. CONCLUSIONS: There were clear distinct differences in tooth-site-specific and caries-severity microbiome diversity patterns and bacterial abundance profiles in S-ECC children.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".