<b>Probiotics as oral health biotherapeutics</b>
Bibliographic record
Abstract
INTRODUCTION: Oral health is affected by its resident microorganisms. Three prominent oral disorders are dental caries, gingivitis and periodontitis, with the oral microbiota playing a key role in the initiation/progression of all three. Understanding the microbiota and the diseases they may cause is critical to the development of new therapeutics. This review is focused on probiotics for the prevention and/or treatment of oral diseases. AREAS COVERED: This review describes the oral ecosystem and its correlation with oral health/disease. The pathogenesis and current prevention/treatment strategies of periodontal diseases (PD) and dental caries (DC) are depicted. An introduction of probiotics is followed by an analysis of their role in PD and DC, and their potential role(s) in oral health. Finally, a discussion ensues on the future research directions and limitations of probiotics for oral health. EXPERT OPINION: An effective oral probiotic formulation should contribute to the prevention/treatment of microbial diseases of the oral cavity. Understanding the oral microbiota's role in oral disease is important for the development of a therapeutic to prevent/treat dental diseases. However, investigations into clinical efficacy, delivery/dose optimization, mechanism(s) of action and other related parameters are yet to be fully explored. Keeping this in mind, investigations into oral probiotic therapies are proving promising.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".