Beneficial microbes for the oral cavity: time to harness the oral streptococci?
Bibliographic record
Abstract
Indigenous microbes are known to influence human health outcomes and various approaches are now being made to positively modulate these microbe-induced outcomes via the administration of probiotics. The application of probiotics that are specific to the oral cavity is a relatively undeveloped field, and their emergence has largely occurred as a reasoned follow-up to initial studies in which probiotics that had already been developed and obtained regulatory approval for intestinal applications were then also evaluated for their putative influence on oral microbiota functionality. These attempts to extend the application of existing probiotics were probably at least in part motivated by recognition of the substantial safety and regulatory hurdles that must be overcome prior to the introduction of a novel probiotic agent. Nevertheless, from an efficacy perspective it appears more logical to develop microbes of oral origin as the specific providers of probiotic solutions for oral diseases, rather than attempting to adapt intestinally-derived strains for this role. Oral bacteria and their bioactive molecules have evolved to operate optimally in this environment and in some cases are known to persist only in oral sites. Amongst the bacteria of more than 700 species now identified within the human oral microbiota, it is the streptococci that are numerically predominant. Although this review highlights the development of the oral cavity bacterium Streptococcus salivarius as an oral probiotic, a number of other streptococcal species have also been shown to have considerable potential as probiotic candidates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".