Evaluation of Microleakage in Resin Composites Bonded to an Er:YAG Laser and Bur-Prepared Root and Coronal Dentin Using Different Bonding Agents
Bibliographic record
Abstract
Introduction: This study was conducted to assess the microleakage in Er:YAG laser-ablated and bur-prepared root and coronal dentin cavities using self-etch and total-etch adhesive systems. Methods: Sixty extracted caries-free human third molars were sectioned for dentin exposure. Then, two standard class V cavities were prepared in the root and coronal dentin of each tooth and allocated to one of the following conditioning groups randomly (n=12/Group): G1: Diamond bur for cavity preparation and single bond (BESB) etch-and-rinse adhesive for bonding, G2: Er:YAG laser (160 mJ, 20 Hz, 29.88 J/cm2 ) and SB (LESB), G3: Er:YAG laser and SB without acid etching (LSB), G4: Diamond bur and Clearfil SE Bond (BCSE) self-etch system, and G5: Er:YAG laser and Clearfil SE Bond (LCSE). The cavities were filled with Z100 composite resin. Dye penetration was assessed after thermocycling. Data analysis was done by Kruskal–Wallis and Mann–Whitney U tests. Statistical significance was set at P<0.05. Results: The results showed there were no statistically significant differences in microleakage between the two preparation methods (bur and laser) or the bonding agents applied (P>0.05). Regardless of the cavity preparation method, dye penetration was significantly higher in coronal dentin than in root dentin (P<0.05). Conclusion: The Er:YAG laser had the same efficacy as the conventional method for cavity preparation, and microleakage did not depend on the bonding agent. Microleakage was significantly higher in coronal restorations than in root restorations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".