Distribution of Dental Caries in 12-Year Old Children of Chandigarh using DMFT and SiC Index- A Cross-Sectional Study
Bibliographic record
Abstract
Aims: To evaluate the distribution of dental caries using the DMFT index (Decayed, Missing, Filled Teeth) and Significant Caries Index (SiC) in 12-year old children of Chandigarh. To comparatively analyze the risk factors between the two groups - ‘SiC index group' and ‘least DMFT group' and to determine if the WHO Health goals have been achieved for the population. Methods: The examination for dental caries was done as per the WHO recommendations on 495 children. DMFT, SiC values and the oral health behaviours were recorded and the risk predictors for caries identified on logistic regression analysis. Results: The prevalence of dental caries was 74.1%. The mean DMFT was 2.93 ± 2.57 (0-12) and the mean SiC was 5.76 ± 1.89 (4-12). The odds of being in the SiC group were lesser with ≥ once a day tooth cleaning (OR:0.644, CI:0.109 - 3.822, p-value 0.628) and higher with ≤ once a day sugar intake (OR:1.286; CI:0.782-2.115, p-value 0.322), ≤ once a day fruit intake (OR: 1.485; CI:0.820 - 2.691, p-value 0.192), in boys (OR:1.175;CI:0.748 - 1.847, p - value 0.484) and in the lower strata (OR:2.578; CI:1.187 - 5.598, p-value 0.017). Conclusion: The study confirmed an unequal distribution of dental caries and aims to focus on the more susceptible lower strata and reduction of the D component of DMFT. The WHO Health Goals for 2000 were achieved for the population, but the Goals for 2015 were yet to be met. The study also highlights the need to strive to attain the WHO Health Goals for 2020.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".