The rise of advanced bioactive restorative materials: Are they redefining operative dentistry?
Bibliographic record
Abstract
OBJECTIVES: This article proposed specific functional characteristics for developing groundbreaking Advanced Bioactive Restorative Adhesive Materials - "ABRAM" and how they could redefine the future of operative dentistry. It emphasised their capacity, particularly when used with biomimetic agents, to enhance dentine remineralisation, seal the resin-dentine interface and possibly extend the longevity of dental restorations. Their potential antibacterial properties and pulp biofunctionality were also discussed. METHODS: The literature analysis focused on experimental and commercial "bioactive" restorative materials, including ion-releasing resin-based materials (e.g., adhesives and composites), as well as bioactive fillers such as bioactive glasses, tailored calcium phosphates, and silicates. It emphasised studies assessing remineralisation potential, interfacial sealing, antibacterial, and bioreductive dentine regeneration properties. RESULTS: Many dental materials release ions and have temporary antibacterial effects, but few show 'bioactive effects' like inducing mineral deposition, restoring collagen, and providing prolonged antibacterial and bioinductive properties for pulp cells, which help dentine formation or pulp vitality. Current restorative methods fail to effectively remineralise demineralised dentine, repair gaps, or prevent secondary caries. 'Bioactive' materials essentially perform like conventional ones, with little evidence of better outcomes. CONCLUSIONS: Innovative ABRAM with antibacterial, remineralising properties, as well as pulp biofunctionality should be developed to improve dental restoration durability. No restorative material on the market can be classified as bioactive. Indeed, the clinical use of "bioactive" materials is limited by a lack of evidence, and unclear effects. Interdisciplinary collaboration is essential to connect laboratory innovation with clinical practice, allowing the dental community to benefit from ABRAM. SIGNIFICANCE: Integrating innovative ABRAM and biomimetic analogues of remineralisation may offer the potential for minimally invasive and longer-lasting biologically-driven restorative treatments. Establishing accepted definitions and in vitro and in vivo methodologies for bioactivity assessment is crucial for validating the therapeutic claims of these materials and supporting their evidence-based adoption.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 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".