Association between salivary level of infection with Streptococcus mutans/Lactobacilli and caries-risk factors in mothers.
Bibliographic record
Abstract
AIM: Understanding factors in mothers associated with high and low salivary levels of Streptococcus mutans and Lactobacilli is an important strategy for early childhood caries prevention. Aim of the study was to identify the association between salivary levels of Streptococcus mutans/Lactobacillus and potential caries risk factors in mothers. MATERIALS AND METHODS: Cross-sectional design used a voluntary sample of 300 mothers of young children. Close-ended questions and observations were used to identify mothers' potential caries risk factors. The presence of Streptococcus mutans and Lactobacilli was determined using the CRT bacteria test (Ivoclar Vivadent). All collected information was converted into frequency and proportion describing the prevalence factor in correlation with Streptococcus mutans and Lactobacilli cariogenic bacteria levels of infection. Results Sample participants showed a high caries risk based on socioeconomic, behavioural and clinical factors. RESULTS: also showed high levels (>105) of Streptococcus mutans and Lactobacilli infections among 28% of mothers. Three factors were significantly associated with Streptococcus mutans infection: level of education, past caries experiences, and observable dental plaque, whereas, a fourth factor, frequency of daily tooth brushing, was associated to Lactobacilli infection. CONCLUSION: This study showed that easily collectible informations such as maternal level of education, frequency of daily tooth brushing and past clinical factors tend to be associated with high level of Streptococcus mutans and Lactobacilli infections in caregivers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".