A Narrative and Meta‑Analytic Study of in vivo Efficiency of the Bioactive Compounds of Propolis in Tooth Decay
Bibliographic record
Abstract
Background: Propolis is one of the major components produced by honeybee. It is well known in different parts of the world such as Iran, Canada, Yemen, Czech Republic, Ethiopia, Bulgaria, Portugal, India, Turkey, Malaysia, the United States of America, Chile, Brazil, and Indonesia. The bioactive constituent of every type of propolis varies depending on the geographical location. Terpenoids, flavonoids, and polyphenol compounds were found to be common in all kinds of propolis. It possess numerous applications such as control of dental infections, plaque cleaning, treating gingivitis, exhibiting antimicrobial effect and treating radiation‑induced oral mucositis and cariogenic infections in caries‑active patients. Methodology: This study thus aimed to undertake a meta‑analysis of the efficacy of bioactive compounds of propolis in tooth decay. A total of three in vivo studies were systematically reviewed, and two studies with a total of 300 pathogen‑free female Wistar rats were included in the final meta‑analysis. Results: The results were compared among three subcategories of smooth surface caries and sulcal caries (slight, moderate, and severe), supporting a statistically significant (P = 0.006) beneficial effect of using fractional propolis. Conclusion: Most of the included studies were preliminary, without blind study and lack of information about standard animal housing protocol. More in vivo and clinical trials of bioactive compounds of propolis should be encouraged in future.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.024 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".