Role of the smear layer in adhesive dentistry and the clinical applications to improve bonding performance
Bibliographic record
Abstract
Currently, dental adhesives can be divided into two systems; a smear layer-removal approach with etch-and-rinse adhesives or a smear layer-modified approach with self-etching adhesives. After phosphoric acid etching, the smear layer is completely removed. More attention is, however, required when using self-etching adhesives. The smear layer is partially demineralized by the weak acidic monomer and subsequently incorporated into the hybrid layer. Therefore, the characteristics of the smear layer play an important role on the bonding performance of self-etching adhesives. Such characteristics, for instance, smear layer thickness and smear layer density, are influenced by many factors, e.g., instruments used for dentin surface preparation, cutting speed, and the abrasive particle size of the cutting instruments. This review discusses the contributing factors that affect the smear layer characteristics, and the influence of the smear layer on the bonding performance of dental adhesives. Also, the application techniques regarding how to improve the bonding performance of self-etching adhesives – the smear layer removal by using chemical agents, or the modification of the adhesive application procedures – are provided.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".