Early childhood caries in multilingual community
Bibliographic record
Abstract
Objective: The aim of the study was to determine the prevalence and severity of early childhood caries (ECC) among different social groups of preschool children in ethnic and multilingual diverse community such as South Backa District (SBD), Republic of Serbia. Methods: This survey was a cross-sectional analytical study and sample type has been projected by simple random choice of 13-71 months old children together with their parents of varied socioeconomic status (SES) and nationality in SBD. Preschool children were randomly selected through a two-stage cluster sampling procedure and parents were invited to answer the questionnaire regarding SES, native language, education, and income issues. The WHO caries diagnostic criterion was used and the severity of ECC was defined by Wine's modified criteria. The data was further analyzed using the SPSS for Windows Microsoft Excel, version 21. The percentage of caries-free children was compared using the χ2 test, and one-way ANOVA was used to compare the mean disease indices at the 5% significance. Results: A total of 341 children were examined and the prevalence of ECC was 30.5%. "White spot" initial caries lesions were the most prevalent form and the highest disease frequency was found in male children (p = 0.038), who did not speak official Serbian language (p = 0.009), in children of the part-time employed parents (p < 0.001), with elementary education (p = 0.001), in low monthly income families (p = 0.023), and Roma ethnic group. Conclusions: The complex interaction of risk factors like social environment along with ethnicity and Serbian language ignorance had a significant influence on the prevalence and severity of ECC in preschool children of this multi-ethnic region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".