The Impacts of Probiotics Supplementation on the Treatment of Periodontitis: An Umbrella Meta-Analysis
Bibliographic record
Abstract
CONTEXT: Probiotics supplementation has many beneficial impacts on periodontitis outcomes. OBJECTIVE: This umbrella meta-analysis evaluated the effectiveness of probiotics in treating periodontitis, focusing on primary outcome measures, including reductions in probing pocket depth (PPD), clinical attachment level (CAL), bleeding on probing (BOP), plaque index (PI), gingival index (GI), and PDD reduction in medium and deep pockets. DATA SOURCE: The Scopus, PubMed, Web of Science, Embase, and Google Scholar online databases were searched up to January 2024. DATA EXTRACTION: All meta-analyses evaluating the impact of probiotics supplementation on periodontitis outcomes were considered eligible. DATA ANALYSIS: A random-effects model was employed to estimate the pooled results. Subgroup and sensitivity analyses were conducted. The quality of the studies was evaluated using the AMSTAR2 tool. Overall, 19 meta-analyses with 67 data sets were included in this umbrella review. Our findings indicated that probiotics supplementation significantly decreased BOP (effect size [ES]: -8.20; 95% CI: -15.10 to -1.31; P = .02) and CAL after sensitivity analysis (ES: -0.52; 95% CI: -0.75 to -0.28; P < .05). No other significant effect was observed on PPD, PI, GI, or PDD reduction in medium and deep pockets. CONCLUSION: This umbrella meta-analysis found that, during their treatment process, probiotics supplementation may positively affect BOP and CAL in people with periodontitis.
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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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".