Modified Implant Surface with Slower and Less Initial Biofilm Formation
Bibliographic record
Abstract
BACKGROUND: Peri-implant mucositis and peri-implantitis are a raising issue in dental implantology. Peri-implant infections are mainly caused by the formation of biofilm. Different surface textures exhibit various conditions for biofilm formation resulting in several speed of maturation and development. MATERIALS AND METHODS: On three different titanium implant surfaces, machined-surface (M), sandblasted large grit, and acid-etched surface (SLA) and machined-modified acid-etched surface (mod MA) initial biofilms were collected. Plaque formation was investigated by erythrosine staining and energy-dispersive X-ray spectroscopy (EDX). For testing the biocompatibility of these plaque-settled surfaces, autoclaved specimens were settled with human gingival fibroblasts, and cell viability was tested. RESULTS: The mean initial plaque surface was detected in the following descending order: M > SLA > mod MA. The differences between these groups were significant. The highest cell viability was detected in the M groups, whereas mod MA and SLA showed comparable results. The results of initial biofilm formation were proved by EDX. CONCLUSIONS: Within the limitations of this study, conclusion can be made that mod MA surface shows significant slower initial biofilm formation which could be an advantage in initial transgingival healing process and also an easement for oral hygiene of patients because maturation of plaque is retarded, and immature biofilms are easier to remove.
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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 | 0.001 |
| 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) | 0.003 | 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".