Microstructure of Titanium‐Cement‐Lithium Disilicate Interface in <scp>CAD‐CAM</scp> Dental Implant Crowns: A Three‐Dimensional Profilometric Analysis
Bibliographic record
Abstract
BACKGROUND: Peri-implantitis is an infection of the implant surface caused by adhesion of bacteria that generate bone resorption and sometimes even consequent implant loss. Both screw-retained and cemented fixed implants are affected. PURPOSE: The purpose of this study is to investigate the morphological defects at the cemented interface between titanium abutment and ceramic crown, comparing different adhesive cements used to fill the marginal gap. MATERIALS AND METHODS: Twelve computer-aided design-computer-aided manufacturing dental crowns were cemented to titanium abutments using three different resin composite cements. Sealed margins were polished using grommets with descending diamond particle size. Three groups of four crowns each were made according to the cement used, namely RelyX Unicem (3 M ESPE), Panavia F 2.0 (Kuraray), and NX3 (Nexus Kerr). Samples were analyzed using optical inspection, three-dimensional profilometry, and image analysis, including analysis of variance. RESULTS: Although RelyX showed significantly lower root mean square surface roughness (4.4 ± 1.5 μm) than that of NX3 (7.0 ± 2.9 μm), it showed no significant difference with Panavia (3.7 ± 1.5 μm). The marginal gap was significantly wider in Panavia (149 ± 108 μm) as compared with NX3 (71 ± 45 μm) and Relyx (64 ± 34 μm). For all groups, homogeneous heights of both metal-cement and ceramic-cement gaps were observed. Moreover, all samples showed homogeneity of the margins and absence of instrumental bias, thus validating both procedure and materials. CONCLUSIONS: When using the chosen polishing method, RelyX Unicem showed both low roughness and marginal width, and thus the smoothest and more continuous abutment-crown interlayer, promising a low probability of occurrence of peri-implantitis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".