The impact of prebiotics and probiotics on the oral microbiome of individuals with periodontal disease: a scoping review.
Bibliographic record
Abstract
Background: The influence of prebiotics and probiotics on oral microbiome composition, addressing dysbiosis, and aiding in the regulation of the immune-inflammatory response has recently been discussed. The objective of this scoping review is to explore current literature that examines the use of prebiotics and probiotics as adjunctive therapy for the treatment of periodontal disease with the intent to identify gaps in the literature to inform future research and dental hygiene practice. Methods: This review was conducted from December 2022 to August 2023 using the Arksey and O'Malley approach and PRISMA-ScR guidelines. Three databases were searched using combinations of keywords. Only peer-reviewed human/in vitro studies published in the last 10 years were included. Results: The search retrieved 204 articles. Duplicates were removed, titles and abstracts screened, and the full text of 80 articles examined, resulting in the inclusion of 19 articles. Discussion and Conclusion: Most of the included literature indicated that probiotics have a positive impact on periodontal health as evidenced by changes in periodontal disease parameters. Future research should further examine various modes of administration and dosages. The effects of specific prebiotic and probiotic strains on specific pathogenic bacteria in conjunction with non-surgical periodontal therapy should also be further explored.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".