Quantitative in vitro comparison of the thrombogenicity of commercial dental implants
Bibliographic record
Abstract
BACKGROUND: Dental implants often have surface modifications that alter surface topography and chemistry to improve osseointegration and thereby increase treatment predictability. Surface contact-induced blood coagulation is associated with the onset of osseointegration. PURPOSE: To quantitatively evaluate the thrombogenicity of two commercially available dental implants that have similar surface roughness but different surface chemistry. MATERIAL AND METHODS: Two commercially available dental implants with anodized or sandblasted acid-etched surfaces were evaluated for thrombogenic properties. Thrombogenicity was assessed by incubating implants for 1 hour in fresh, partially heparinized blood followed by hemocyte quantification, microscopic evaluation, and quantification of thrombogenic biomarkers. RESULTS: Fibrin coverage was significantly higher on the anodized surface compared with the sandblasted acid-etched surface (P < 0.0001). Platelet and white blood cell attachment followed a similar pattern. The increased thrombogenicity was confirmed based on a significant increase in the levels of the coagulation cascade biomarkers, thrombin antithrombin complex, and β-thromboglobulin (all P < 0.05). CONCLUSION: Dental implants with comparable roughness but differing surface chemistry had differing extents of blood contact activation. These data suggest that surface chemistry from anodization augments implant thrombogenicity compared with that from sandblasting and acid-etching, which could have implications for osseointegration.
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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 | 0.000 | 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".