Association between environmental tobacco smoke and dental caries amongst 5-14 years old children in Karachi, Pakistan.
Bibliographic record
Abstract
OBJECTIVE: To determine the association between environmental tobacco smoke and dental caries. METHODS: This cross-sectional study was conducted in peri-urban and urban areas of Karachi, from February to August 2014, and comprised children aged 5-14 years. A pre-coded questionnaire for environmental tobacco smoke and food frequency questionnaire for dietary habits were used. Dental examination of children was done to detect caries. Cox-proportional hazard algorithm was used to measure the association of environmental tobacco smoke with dental caries at multivariable level. STATA version 12.0 was used for statistical analysis. RESULTS: Of the 500 children, 250(50%) each were from peri-urban and urban localities. The prevalence of dental caries was 336(67.2%).Family members of 154(30.8%) participants reported smoking. After adjusting for junk food intake, in-between meals, age, plaque index, dental visits and socio-economic status, the association between environmental tobacco smoke and dental caries remained statistically significant (p<0.05). Compared to non-exposed children, the adjusted prevalence ratio was 1.25 (95% confidence interval: 1.08-1.46) and 1.36 (95% confidence interval: 1.09-1.70) for children with < 30 minutes and >30 minutes of environmental tobacco smoke exposure, respectively. CONCLUSIONS: Environmental tobacco smoke was found to be associated with dental caries.
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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 | 0.000 | 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".