Improving Oral Health with Fluoride-Free Calcium-Phosphate-Based Biomimetic Toothpastes: An Update of the Clinical Evidence
Bibliographic record
Abstract
As the demand for clinically effective fluoride-free oral care products for consumers increases, it is important to document which types of toothpastes have been shown in clinical studies to be effective in improving oral health. In this review, we included different indications, i.e., caries prevention, improving periodontal health, reducing dentin hypersensitivity, protecting against dental erosion, and safely improving tooth whitening in defining what constitutes improvement in oral health. While there are several professional and consumer fluoride-containing formulations fortified with calcium-phosphate-based ingredients, this review focuses on fluoride-free toothpastes containing biomimetic calcium-phosphate-based molecules as the primary active ingredients. Several databases were searched, and only clinical trials in human subjects were included; in vitro and animal studies were excluded. There were 62 oral health clinical trials on biomimetic hydroxyapatite (HAP), 57 on casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), 26 on calcium sodium phosphosilicate (CSPS, or so called Bioglass), and 2 on β-tricalcium phosphate (β-TCP). HAP formulations were tested the most in clinical trials for benefits in preventing caries, dentin hypersensitivity, improving periodontal health, and tooth whitening. Based on the current clinical evidence to date, fluoride-free HAP toothpaste formulations are the most versatile of the calcium phosphate active ingredients in toothpastes for improving oral 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".