Anaerobic Adhesive Effect on the Counter‐Torque of Zirconia Implant Abutment Screws: In Vitro Study
Bibliographic record
Abstract
INTRODUCTION: Implantology has become a primary solution for tooth loss due to excellent osseointegration and high long-term success rates. However, complications such as abutment screw loosening, especially in implant-supported single crowns, compromise prosthesis longevity. Anaerobic adhesives (AAs) have shown promise in mechanical fields for preventing screw loosening, but their effectiveness in dental implants, particularly zirconia, remains uncertain. This study evaluates the effect of medium-strength AA on the counter-torque and screw stability of zirconia implant abutments. METHODS: Twenty neodent Zi zirconia implants were divided into two groups: control (n = 10) without adhesive and experimental (n = 10) using medium-strength AA (Loctite 242) on the prosthetic screw. Abutment screws were torqued to 32 Ncm and underwent mechanical cycling simulating mastication. Counter-torque was measured post-cycling. Structural damage and adhesive residues were inspected using a stereomicroscope. Data were analyzed using descriptive statistics including means and standard deviations (SDs). The Shapiro-Wilk test was performed to assess data normality. The counter-torque values after cycling were compared to the installation torque value using the one-sample t-test. Comparison between groups was performed using Student's t test. Statistical significance was established as p < 0.05. RESULTS: The presence of AA was detected on all screws, without causing damage and was easily removable. Both groups showed significantly lower counter-torque values than the installation torque, with no significant intergroup difference. CONCLUSION: Medium-strength AA did not significantly affect the counter-torque values of zirconia implant abutment screws, although it was easily removable and caused no damage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".