Cleaning potential of different air abrasive powders and their impact on implant surface roughness
Bibliographic record
Abstract
Abstract Background Implant surface roughness after air abrasive therapy has not been measured precisely in previous research. Debridement with air abrasion facilitates the mechanical removal of bacterial biofilms but may damage implant surfaces on a microscopic level. Purpose This study aimed to investigate the cleaning potential of various air abrasive powders and their effect on titanium implant surfaces. Materials and Methods Twenty implants coated with red ink were inserted into three‐dimensional printed circumferential bone defect models. Treatment was completed with three types of air abrasive powders: sodium bicarbonate (SB), glycine, and erythritol for 60 seconds. Water alone was used as control. The percentage of remaining ink was assessed using digital photography and graphic software. Implant surface topography/roughness was quantified using optical profilometry and examined via scanning electron microscopy. The microscopic analysis was performed at two implant areas: collar (Laser‐Lok surface) and threads. Results The cleaned surfaces (%, mean ± SD) after treatment with SB, glycine, and erythritol accounted for 49.3 ± 3.6%, 33.1 ± 1.2%, and 25.1 ± 0.7%, respectively. Statistically significant differences were found between all groups ( P < .001). SB was the only powder that significantly increased the implant roughness ( S a ) on both the implant collar (1.53‐2.10 μm) and threads (3.53‐4.20 μm). Regardless of the abrasive powder used, the collar, emerging implant surfaces from the defect base, and surfaces beneath implants threads exhibited more post‐treatment residual ink. Conclusion Large‐sized powder showed the greatest cleaning capacity, but caused more alterations to the implant surface. Glycine and erythritol displayed no significant changes in surface roughness, however, demonstrated a limited ink removal capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".