Prevalence and associated factors to developmental defects of enamel in primary and permanent dentition.
Bibliographic record
Abstract
BACKGROUND: The disturbances during enamel formation manifesting as Developmental Defects of Enamel (DDE) present important clinical significance since they are responsible for aesthetic problems, dental sensitivity and may act as predisposing factor for dental caries. The aim of the present study was to examine the prevalence of DDE and associated etiological factors. MATERIALS AND METHODS: A total of 1550 children was examined, using a mouth mirror and a CPI probe. Diagnosis of DDE was established according to the modified DDE index. Relationships between DDE and body mass index (BMI), socioeconomic status (SES), childhood illness and birth weight were assessed using the multivariable logistic regression. Difference in proportion was tested using Kruskal-Wallis H, followed by Mann-Whitney U test for inter group comparison, and Chi-Square tests. RESULTS: The prevalence of DDE was 42.19%. The logistic regression model showed that there was a significant association of DDE with age (p<0.05), gender (p<0.05), low SES (p<0.05) and obesity (p<0.001). Demarcated opacity was the most frequent type of DDE both in primary and permanent dentition. Prevalence was more frequent in permanent dentition compared to primary dentition, with the permanent maxillary central incisor and primary maxillary second molars being the teeth affected most commonly. CONCLUSIONS: Prevalence of DDE was more in permanent teeth compared to primary teeth. A significant association of DDE with gender, low SES and BMI was demonstrated in the present study.
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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 | 0.001 | 0.001 |
| 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".