An In Vivo Investigation of Non-Metallic vs. Metallic Hand Scalers on Zirconia Implant-Supported Crowns: A Year-Long Analysis of Peri-Implant Maintenance
Bibliographic record
Abstract
This study examined whether the degree of abutment surface modification that may occur with regular periodontal instrumentation has a clinical impact in terms of increased plaque accumulation and increased peri-implant tissue inflammation on zirconia implant abutments. Thirteen patients who had zirconia implant crowns were recruited in this randomized clinical trial. Each patient acted as their control and had either the buccal or lingual surface of their screw-retained implant restoration scaled with a metallic scaler and the other surface with a non-metallic scaler at 3, 6, 9, and 12 months. Cytokine testing of the peri-implant crevicular fluid was completed at 0, 3, and 12 months for IL-2, IL-4, IL-6, IL-8, IL-10, TNF-α, or IFNγ. Implant crowns were removed at 12 months and evaluated under an atomic force microscope for the average roughness (Ra). The implant crowns were polished and re-inserted. The results were analyzed using the Kruskal-Wallis test, and post hoc tests were conducted with a significance level of α = 0.05. Significant differences in surface roughness (Ra) were observed between the metallic and non-metallic scalers. The median Ra values were 274.0 nm for metallic scalers and 147.1 nm for non-metallic scalers. However, there were no significant differences between the type of scaler used and the amount of clinical inflammation or cytokine production. Metallic scalers produced deeper, more aggressive surface alterations to the abutment/crown zirconia surface, but there was no statistically significant difference between the degree of surface alterations, amount of BOP, and the amplitude of cytokine inflammation produced.
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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 | 0.000 |
| 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".