Artificial biofilm removal in a peri‐implant mucositis model: Efficacy of air polishing technology as adjunct to ultrasonic debridement alone and impact of the site and the depth of mucosal tunnel—An in vitro study
Bibliographic record
Abstract
AIM: The current in vitro model aims to evaluate the adjunctive effect on artificial biofilm removal determined by the use of a glycine-powder air-polishing procedure (GPAP) over the ultrasonic debridement (USD) alone when the removal of artificial biofilm on abutment surface is performed. The procedures were carried out also evaluating the impact of the site (mesial, distal, vestibular, and oral) and three different mucosal tunnel depths (2 mm, 4 mm, and 6 mm). MATERIALS AND METHODS: Single tooth implant replacement was simulated. Three different abutment heights together with a prosthetic contour were investigated (2 mm, 4 mm, and 6 mm); custom-made gingival masks were created to mimic peri-implant soft tissue. Biofilm was simulated with an indelible ink. The protocol consisted in two intervention stages for each abutment: (a) USD with PEEK tip plus (b) GPAP. At the end of each intervention, abutments were unscrewed, and standardized photographs were taken. Statistical analysis was carried out to compare residual stain percentage between the two intervention stages and among different sites and mucosal tunnels. RESULTS: A total of 30 abutments were instrumented. A significant reduction of the percentage of residual staining (PRS) after the combination of GPAP + USD over USD alone was demonstrated (16% vs. 32%; p < 0.05). Moreover, the better performance of the GPAP + USD protocol was observed regardless of the different mucosal tunnel heights and the sites analyzed. Intragroup analysis unveils that the smaller PRS was observed for shallow mucosal tunnels (2 mm) and vestibular sites for both protocols. CONCLUSION: GPAP + USD provided adjunctive effect on artificial biofilm removal in comparison to the USD alone. Furthermore, proximal surfaces and deeper mucosal tunnels (4 and 6 mm) showed a reduced instrumentation efficacy for both protocols.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".