Effects of pomegranate extract on preventing dental caries: a systematic review
Bibliographic record
Abstract
Background/purpose The present study aimed to evaluate the effect of pomegranate extract on the prevention of dental caries compared to standard care, placebo, and no intervention. Materials and methods A bibliographic search in four databases, including PubMed, Scopus, Google Scholar, and CENTRAL, yielded 291 studies until September 8, 2023. The search was performed among the studies written in English using the search terms “Punica granatum” AND (“dental caries” OR “Streptococcus mutans” OR “tooth demineralization”) After screening the titles/abstracts and full texts of these studies, 7 articles were chosen. Results In all 7 articles, pomegranate mouthwash was used as the intervention. In 5 studies, the control group used 0.2% chlorhexidine (CHX) mouthwash. Additionally, 4 articles reported a reduction in the mean Streptococcus mutans plaque count in both groups; however, better results were observed in the CHX mouthwash group. In one study, no significant difference was reported between the study and control groups. Finally, one study showed the significant superiority of a hydroalcoholic extract of pomegranate mouthwash over CHX mouthwash. Conclusion Overall, the results suggest that pomegranate extract mouthwash is highly effective in reducing caries-causing bacteria. No side effects were reported for pomegranate use in these studies. Systematic Review Registration https://osf.io/69gpc/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.007 |
| 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".