Role of the smear layer in adhesive dentistry and the clinical applications to improve bonding performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it