Comparison of nano-silver fluoride, nano-hydroxyapatite and sodium fluoride varnishes in prevention and arrestment of initial carious lesions: a randomized controlled clinical trial
Bibliographic record
Abstract
Introduction: Globally, the increased prevalence of dental caries in children makes the need for dental remineralizing agents necessary.To the best of our knowledge there are a few published clinical trials on the effectiveness of nano-re-mineralizing agents but without comparing between them.Objectives: To compare nano-silver fluoride (NSF), nano-hydroxyapatite (n-HAP) and sodium fluoride (NaF) varnishes in prevention and arrestment of initial enamel caries on young permanent teeth.Material and methods: One hundred and fifteen children with ages ranging from 7 to 10 years were randomly divided into three groups: the NSF group (n = 39), the n-HAP group (n = 37) and the NaF group (n = 39).The clinical evaluation of dental surfaces was done by one masked examiner using ICDAS II to detect the number of new and arrested initial lesions.Varnish application was done annually for NSF, but it was done biannually for n-HAP and NaF.This was followed up at 3, 6 and 12-month intervals.The Kruskal-Wallis test was used to compare the groups.Results: After a follow-up period of 12 months, the NSF group had a significantly lower median number of new carious lesions, 1 (0), than the n-HAP group, 2 (3), with p = 0.01.There were significantly more arrested lesions in both NSF and NaF than in n-HAP at all visits, with p = 0.003, 0.01 and 0.002.At the level of children, the NSF group had the lowest number of children who were affected by new caries (23.1%) and the highest number of children who have arrested initial caries (87.2%).Conclusions: Annual application of NSF varnish might be recommended as an effective method of dental caries prevention and treatment of initial caries lesions.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".