Exploring Alternative Fluoride Agents for Dental Caries Prevention: A Systematic Review
Bibliographic record
Abstract
Background: Dental caries remains a widespread global health challenge, prompting the exploration of alternative fluoride agents. This systematic review evaluates the efficacy of nano-silver fluoride (NSF), silver diamine fluoride (SDF), titanium tetrafluoride (TiF4), and sodium fluoride varnish (NaF) in caries prevention and arrest. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted to identify studies published between 2010 and 2024. Inclusion criteria focused on randomized controlled trials, clinical studies, and in vitro investigations comparing alternative fluoride agents. Data on caries arrest rates, enamel remineralization, biofilm inhibition, and adverse effects were extracted and analyzed. Results: NSF demonstrated superior enamel microhardness and reduced microleakage without staining. SDF showed high caries arrest rates (95-98%) but caused black staining of treated lesions. TiF4 effectively increased acid resistance and reduced biofilm activity, outperforming NaF in specific applications. NaF, while cost-effective and widely accessible, was less effective for advanced caries compared to SDF and NSF. Conclusion: Alternative fluoride agents, such as NSF and TiF4, offer promising results in caries prevention and management, addressing limitations of traditional fluoride treatments. Further clinical trials are needed to validate these findings for broader implementation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".