An Assessment of the Prevalence of Dental Caries, Oral Hygiene Status, Deft Index, and Oral Hygiene Habits Among Children With Special Healthcare Needs
Bibliographic record
Abstract
Background and objective Children with special healthcare needs are at an increased risk of oral health problems, including dental caries. Understanding the prevalence of dental caries, oral hygiene status, deft (decayed, extracted, filled teeth) index, and oral hygiene habits in this population is crucial for effective oral healthcare planning and interventions. The aim of this study was to assess the prevalence of dental caries, oral hygiene status, deft index, and oral hygiene habits among children aged 4-15 years with special healthcare needs in Jodhpur District, Rajasthan, India. Methods A cross-sectional study was conducted among 124 children from various, government and non-governmental organizations (NGO)-run special schools. Data on dental caries, oral hygiene status, deft index, and oral hygiene habits were collected using standardized tools and techniques. Descriptive statistics, including frequencies and percentages, were used to analyze the data. Results The prevalence of dental caries among children with special healthcare needs was 65%. The severity of dental caries varied, with 40% classified as mild, 20% as moderate, and 5% as severe. Additionally, 75% of the children exhibited poor oral hygiene, as indicated by the oral hygiene status assessment. The mean deft index score was 2.8, indicating an average dental caries experience among the participants. Regarding oral hygiene habits, 60% reported brushing their teeth once a day, while 40% reported brushing twice a day. However, a significant proportion (70%) reported non-fluoride use, and 55% stated they did not perform regular flossing. Conclusion This study highlights a high prevalence of dental caries, poor oral hygiene status, and suboptimal oral hygiene habits among children with special healthcare needs in Jodhpur District. The findings emphasize the need for targeted interventions focusing on preventive measures, oral health education, and improving access to oral healthcare for this vulnerable population. Further research with larger sample sizes and longitudinal study designs is warranted to validate these findings and develop effective strategies for enhancing oral health outcomes in children with special healthcare needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".