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Record W4415068828 · doi:10.4103/jpbs.jpbs_354_25

Exploring Alternative Fluoride Agents for Dental Caries Prevention: A Systematic Review

2025· article· en· W4415068828 on OpenAlexaff
Nitin Kumar, Shivam Ratti, Yash Jain, Muqthadir Siddiqui Mohammed Abdul, Praveen Kumar Varma Datla, Bharani K. Bhattu

Bibliographic record

VenueJournal of Pharmacy And Bioallied Sciences · 2025
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsFluorideFluoride varnishEnamel paintSodium fluorideClinical trialRandomized controlled trial

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.193
GPT teacher head0.442
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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