Efficiency and Thermal Changes during Implantoplasty in Relation to Bur Type
Bibliographic record
Abstract
BACKGROUND: Implantoplasty is one of the options in treating peri-implantitis. The efficacy of the dental bur used can reduce the time needed for the procedure and, as a consequence, minimize the risk of overheating that can negatively affect the remaining bone surrounding the implant. PURPOSE: The aim of this study was to evaluate the efficacy of three dental burs in removing implant substance (titanium) and to determine the amount of heat generated by each bur. MATERIALS AND METHODS: Four burs with different surface properties (diamond, diamond - Premium Line, carbide, and smooth bur - control [Strauss Co., Raanana, Israel]) were attached to a high-speed handpiece and applied to a titanium implant for a total of 60 seconds after cooling by water spray. Variations in temperature were recorded every 5 seconds, and the amount of implant substance removed (reduction in weight of the implant) was evaluated. RESULTS: The diamond Premium Line bur removed 59.24 mg; carbide, 29.39 mg; diamond, 11.35 mg; and smooth bur (control) 0.19 mg, statistically significant. Only minimum thermal changes (∼1.5°C) were recorded for all four burs. CONCLUSIONS: There are considerable differences in efficiency of different burs working on titanium. Selecting the proper bur can reduce working time. Under proper cooling conditions, implantoplasty does not generate excess temperature increases that can damage soft tissue or bone surrounding the treated implant.
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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 | 0.002 |
| 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 | 0.000 | 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".