Rational design and in vitro characterization of novel dental implant and abutment surfaces for balancing clinical and biological needs
Bibliographic record
Abstract
BACKGROUND: Long-term success and patient satisfaction of dental implant systems can only be achieved by fulfilling clinical as well as biological needs related to maintenance, aesthetics, soft tissue sealing, and osseointegration, among others. Surface properties largely contribute to the biological and clinical performance of implants and abutments. PURPOSE: To decipher the clinical and biological needs in implant dentistry. To address identified needs, next-generation dental implant and abutment surfaces are designed and characterized in vitro. MATERIALS AND METHODS: Novel implant and abutment surface designs were produced and characterized using surface chemical analysis, surface topography analysis, scanning electron microscopy, contact-angle measurements, and cell-culture experiments. RESULTS: The novel anodized implant surface was gradually anodized, increasing the surface roughness, surface enlargement, and oxide-layer thickness from platform to apex. The surface was phosphorus enriched, nonporous, and nanostructured at the collar, and showed micropores elsewhere. The novel anodized abutment surface was smooth, nanostructured, nonporous, and yellow. Pristine surfaces with high density of hydroxyl-groups were protected during storage using a removable cell-friendly layer that allowed dry packaging. CONCLUSIONS: A novel anodized implant system was developed with surface chemistry, topography, nanostructure, color, and surface energy designed to balance the clinical and biological needs at every tissue level.
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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) | 0.001 | 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 | 0.000 | 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 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".