Sugar‐sweetened beverages and periodontal disease: A systematic review
Bibliographic record
Abstract
This systematic review aims to determine the association between the consumption of sugar-sweetened beverages (SSBs) and periodontal disease. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed to conduct a literature search on five electronic databases till January 2022. Systemically healthy individuals consuming SSBs and presenting periodontal disease (gingivitis/periodontitis) were included. The modified Newcastle-Ottawa Scale and the Grading of Recommendation Assessment Development and Evaluation criteria were respectively used to assess the risk of bias and the evidence's quality. Of the 1303 eligible records identified in the initial search, ten studies (nine cross-sectional and one case-control) were selected for the final review. Among the included articles, five reported SSBs intake in the form of carbonated soft drinks, two as sugary drinks, two as soft drinks, and one as coffee with added sugar. Four studies reported gingivitis as an outcome, while the remaining six studies reported periodontitis using validated indices. The included studies were of medium to high quality. Consumption of SSBs may increase gingival bleeding, thereby gingivitis and the risk of periodontitis. Intake of added sugars like SSBs should be considered as a potential factor during gingival/periodontal risk assessment. Further studies are warranted to establish additional evidence of association.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".