Fibrin clot adherence on cleaned and decontaminated titanium abutment surfaces: An in vitro study
Bibliographic record
Abstract
STATEMENT OF PROBLEM: Osseointegration is now primarily established, but soft tissue integration is still susceptible to failure and problematic on implant surfaces. So, implant dentistry is increasingly focusing on improving peri-implant soft tissue integration. PURPOSE: The present study aimed to evaluate the blood fibrin clot formation and adhesion on the abutment after cleaning and decontamination and determine the suitable abutment surface associated with fibrin clot attachment. MATERIALS AND METHODS: Forty-two abutments (14 per group) were used in the present study: a brand-new (BN), contaminated with biofilm (CO) and decontaminated with an enzymatic cleaner and autoclave sterilization (DEC). For a fibrin clot, 9 mL of whole human blood and abutments was centrifuged at 2700 rpm for 12 min. Clots were divided into two parts for histomorphometry and scanning electron microscopy (SEM) analysis. Twelve abutments disconnected from the clot and two not treated with blood were observed under SEM. RESULTS: Residual debris and biofilm were observed on the abutment surface in the CO group but not in other groups. Healthy and organized fibrin clots formed on all abutments. The fibrin extension areas are distributed uniformly in BN and DEC groups but irregularly in CO. The surface percentage of the fibrin clot extensions was 41.76% ± 6.73, 26.99% ± 6.40, and 37.83% ± 9.72 for the BN, CON, and DEC groups, respectively. The blood clot-attached areas in the CO group were statistically lower than the other groups. No difference was observed between the BN and DEC groups. CONCLUSIONS: This study confirmed that surface contamination could influence blood clot attachment on the abutment surfaces. Cleaning and sterilization can have a favorable effect on soft tissue healing on abutment surfaces.
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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) | 0.001 | 0.001 |
| 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".