Effects of silver diamine fluoride on oral bacteriome and mycobiome: a randomized clinical trial
Bibliographic record
Abstract
Abstract Introduction Silver diamine fluoride (SDF) is a simple and non-invasive agent used to arrest early childhood caries (ECC). This study aimed to investigate potential changes to the oral microbiome in children with ECC who were treated with SDF at three different frequency regimens. Methods Forty-five children ( n =15 per group) with ECC were recruited into a randomized clinical trial testing three different treatment frequency regimens of SDF. A total of 195 carious lesions were treated with two applications of 38% SDF and 5% sodium fluoride varnish (NaFV) and assessed over three study visits (one month (Regimen 1M), four months (Regimen 4M), or six months (Regimen 6M) apart). Dental plaque samples were collected at each visit. Sequencing of the V4- 16S rRNA and ITS1 rRNA genes were used to study the supragingival plaque microbiome. Results The overall arrest rates for treated carious lesions were 75.9% at Visit 2 and 92.8% at Visit 3. Arrest rates were higher for all lesions after two applications of SDF with NaFV, and applications one month and four months apart had higher arrest rates (95.9% and 98.5%) than six months (81.1%) apart. The microbial diversity analyses showed no significant differences in the overall microbiome after SDF treatment. However, significant changes in the abundance of specific bacteria and fungi, particularly Lactobacillus spp. , Bifidobacterium spp., and Candida spp. were observed after treatment. Furthermore, overabundance of Streptococcus mutans and Candida dubliniensis at baseline was observed in children who had at least one caries lesion not arrested after one SDF application, compared to those who had 100% arrest rates. Conclusion SDF with NaFV applications were an effective modality for arresting ECC, with higher arrest rates after two SDF applications. No loss of diversity but significant changes in the abundance of specific bacteria and fungi were consequences of SDF treatment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".