Dietary patterns and dental caries in permanent dentition: A scoping review
Bibliographic record
Abstract
Introduction: Dental caries is influenced by diet com-ponents. Dietary patterns are the way people combine foods and drinks. They can be derived through adherence to a standard diet (a priori), obtaining patterns from existing diet data (a posteriori), or Reduced Rank Regression. Dental caries research has focused on sugar instead of food combinations. The aim of this study was to synthesize the evidence about the relationship between caries in permanent dentition and dietary patterns. Materials and Methods: A scoping review was conducted by searching in Web of Science and PubMed. We included articles from 2001 to 2021, that studied dietary patterns or a combination of foods using one of the three methods described. Articles that dealt exclusively with breastfeeding, temporary dentition in children, or specific chronic diseases or disabilities were excluded. We assessed the quality of the articles with the Newcastle Ottawa Scale. Results: 1094 articles were identified and nine were included in qualitative synthesis. Three articles obtained dietary patterns through a priori methods and six with an a posteriori approach. Most of the studies (8) were cross-sectional. Some dietary patterns related to caries were “High in sugar-sweetened beverages and sandwiches”, “obesogenic” and “sweet”. Adherence to dietary recommendations like Alternative Healthy Eating Index-2010 and Dietary Approaches to Stop Hypertension (DASH) were associated with lower DMFS index and root caries index, respectively. Conclusions: An association between dietary patterns and caries was found, but causality cannot be affirmed. To a better understanding of this problem, new investigations are needed that should be focused on dietary styles instead of only some ingredients. Keywords: Dietary patterns; Dental caries; diet quality scores; Principal component analysis; Food combinations; Health behavior.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".