Adjunctive dental therapies in caries-active children: Shifting the cariogenic salivary microbiome from dysbiosis towards non-cariogenic health
Bibliographic record
Abstract
The oral microbiome is a complex assembly of microbial species, whose constituents can tilt the balance towards progression of oral disease or sustained health. Recently we identified sex-specific differences in the salivary microbiome contained within caries-active and caries-free children. In this study, we sought to ascertain if adjunctive dental therapies, including povidone iodine and chlorhexidine, were effective in shifting the cariogenic microbiome from dysbiosis to non-cariogenic health. We recruited young children (ages 2–12 years) to enter five enrollment groups, with each group (N = 9–30 participants/group) receiving caries restorative and/or adjunctive therapies, either singularly or in combination (OHSU IRB #6535). Saliva specimens were collected pre- and post-treatment (4–8 weeks) of caries preventive measures, and oral microbiota were identified using next generation sequencing (HOMINGS, Forsyth Institute, Cambridge, MA). With the use of multi-dimensional scaling plots, support vector machine learning, odds ratio analysis, and other statistical methods, we have determined that treatment with povidone iodine can shift the composition of the salivary cariogenic microbiome to include higher proportions of aerobic microorganisms, such as Stentrophomonas maltophila, as well as non-cariogenic, anaerobic microorganisms including Poryphyromonas and Fusobacterium species. We have identified microorganisms that are associated with caries-active children and have determined that povidone iodine is an effective adjunctive therapy that has the potential to shift the composition of the cariogenic microbiome to one more closely aligned with non-cariogenic health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".