Advanced non-fluoride approaches to dental enamel remineralization: The next level in enamel repair management
Bibliographic record
Abstract
In modern dentistry, a minimally invasive management of early caries lesions or early-stage erosive tooth wear (ETW) with synthetic remineralization systems has become indispensable. In addition to fluoride, which is still the non-plus-ultra in these early caries/ETW treatments, a number of new developments are in the test phase or have already been commercialized. Some of these systems claim that they are comparable or even superior to fluoride in terms of their ability to remineralize enamel. Besides, their use can help avoid some of the risks associated with fluoride and support treatments of patients with a high risk of caries. Two individual non-fluoride systems can be distinguished; intrinsic and extrinsic remineralization approaches. Intrinsic (protein/peptide) systems adsorb to hydroxyapatite crystals/organics located within enamel prisms and accumulate endogenous calcium and phosphate ions from saliva, which ultimately leads to the re-growth of enamel crystals. Extrinsic remineralization systems function on the basis of the external (non-saliva) supply of calcium and phosphate to the crystals to be re-grown. This article, following an introduction into enamel (re)mineralization and fluoride-assisted remineralization, discusses the requirements for non-fluoride remineralization systems, particularly their mechanisms and challenges, and summarizes the findings that underpin the most promising advances in enamel remineralization therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".