Prevalence of Common Dental Problems among Primary School Children in a Rural Area of Mymensingh
Bibliographic record
Abstract
Introduction: In Bangladesh little is known about the prevalence of dental problems, hygiene practices and dietary factors among school age children. Methods: This cross-sectional study was conducted at Ramganj Govt. Primary School, Mymensingh, Bangladesh from June 2022 to May 2023. A total of 79 students were included in the study. The sampling procedure was a purposive sampling. To collect required information personal interview was taken by using a pre-tested questionnaire & dental condition was examined. The collected data were analyzed using SPSS version 20.0. Results: In this study, among 79 students which 47 (58.49%) had tooth problems: 34 dental caries (43.04%), 9 gingivitis (11.39%), 6 dental abscess (7.59%), 10 dental calculus (12.66%), and 1 had periodontal inflammation (1.27%). The problems were overlapping. Tooth-brushing 96-20%, daily bathing 94-94%, hand-washing after defecation were 96-20%, hand-washing before meal 98-73%. Most of them brush once and that is before breakfast. 36.71% consume balanced food. Consumption of calcium, Vitamin D precursors and Vitamin C rich foods were mostly acceptable. Conclusion: Important problems are dental caries and dental calculus. Improvement requires health education, dental care and raising awareness among children. More surveys are needed. Journal of Dentistry and Allied Science, Vol. 8 No 1: 34-42
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".