Could dietary restrictions affect periodontal disease? A systematic review
Bibliographic record
Abstract
OBJECTIVE: This review aimed at evaluating the possible benefits that caloric restriction (CR) may provide to periodontal disease progression and response to treatment. MATERIAL AND METHODS: Electronic search on Medline, Embase and Cochrane, and manual search were performed to identify pre-clinical and on human studies reporting the consequences of CR on clinical and inflammatory parameters related to periodontitis. Newcastle Ottawa System and SYRCLE scale were used to assess the risk of bias. RESULTS: Four thousand nine hundred eighty articles were initially screened, and a total of 6 articles were finally included, consisting of 4 animal studies and 2 studies in humans. Due to the limited number of studies and heterogeneity of the data, results were presented in descriptive analyses. All studies showed that, compared to the normal (ad libitum) diet, CR might have the potential to reduce the local and systemic hyper-inflammatory state as well as disease progression in periodontal patients. CONCLUSIONS: Within the existing limitations, this review highlights that CR showed some improvements in the periodontal condition by reducing the local and systemic inflammation related to the periodontitis and by improving clinical parameters. However, the results should be interpreted with caution since robust research such as randomized clinical trials is still missing. CLINICAL RELEVANCE: This review shows that some dietary/caloric restrictions approaches may have the potential to improve periodontal conditions and, in addition, highlights a need for human studies with a robust methodology in order to draw stronger evidence-based conclusions.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| 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".