Dietary Vitamins and DMFT index in Rafsanjan adults, a Cross- sectional Study on Rafsanjan Adults Cohort Data
Bibliographic record
Abstract
Abstract Introduction: Vitamins are generally known to be important in oral health. Some associations have been found between vitamins and dental caries, but these findings have been controversial so far. This study aimed to investigate the associations of dietary intakes of vitamins and DMFT index. Methods and materials: In this cross-sectional study, the study population was 3028 subjects aged 35-70 years from Rafsanjan Cohort Study's Oral Health Branch (OHBRCS) which is a branch of Rafsanjan Cohort Study (RCS). RCS is a part of the prospective epidemiological research studies in IRAN (PERSIAN). Subjects’ demographic information, variables related to oral health, history of underlying diseases, history of smoking, alcohol, and opium use based on questionnaires and checklists produced by the Persian cohort team was obtained and also dietary intakes of vitamin A, vitamin E and vitamin B family were collected by a validated food frequency questionnaire. Linier regression analysis was used to investigate the association between intake of dietary vitamins and DMFT (Decayed, Missing, and filled Teeth) using crude and adjusted models. Results: The findings showed low levels of education and socio-economic status, older age, smoking and opium consumption, and decreasing the frequency of brushing are significantly associated with an increase in the DMFT index. DMFT index were more unfavorable in people with dietary intake ≤ median of all measured vitamins. In fully adjusted model, DMFT index showed a significant negative relationship with dietary intakes of Vitamin A, β_carotene, lutein_zea xanthin, Vitamin E, Vitamin K, thiamin, Vitamin B6, and folate (Unstd.B =-0.54, 0.63,0.86,0.49,0.88,0.63,0.66,0.54,respectively). Conclusion: Increasing the intake of Vitamin A, β_carotene, lutein_zea xanthin, Vitamin E, Vitamin K, thiamin, Vitamin B6, and folate may be associated with the low DMFT index, so it is recommended to use more this category of vitamins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".