Free Sugar Intake and Periodontal Diseases: A Systematic Review
Bibliographic record
Abstract
High free sugar intake is associated with an increased risk of various non-communicable diseases. We aimed to systematically review articles investigating the association between free sugar intake and periodontal diseases. This systematic review was conducted according to PRISMA guidelines and was registered in the PROSPERO database (CRD42022337828). We obtained articles from PubMed, Web of Science, and Scopus in April 2022. The study selection was performed according to predefined eligibility criteria based on the following PECOS: (P) general population, (E/C) free-sugar-containing food/beverage intake, (O) clinically measured periodontal diseases, and (S) observational study and clinical trial. Of the 839 screened records, 13 studies were included in the review. Most studies (n = 12) had a cross-sectional design. The age groups in the included studies were children/adolescents (n = 5) and adults (n = 8). Among the included studies, 11 reported a significant association between the frequent intake of free-sugar-containing food or beverages and a higher prevalence or incidence of periodontal diseases. The quality of most of the included studies was scored "fair" based on the Newcastle-Ottawa Quality Assessment Scale. Although the majority of the included studies reported a significant positive association between high free sugar intake and periodontal diseases, the evidence is considered to be limited due to the study designs.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".