Thin Bioactive Ceramic‐Coated Alumina‐Blasted/Acid‐Etched Implant Surface Enhances Biomechanical Fixation of Implants: An Experimental Study in Dogs
Bibliographic record
Abstract
BACKGROUND: Thin bioceramic coatings have been regarded as potential substitutes for plasma-sprayed hydroxyapatite coatings. PURPOSE: This study tested the hypothesis that a thin bioactive ceramic coating deposition on an alumina-blasted/acid-etched (AB/AE) surface would positively affect the biomechanical fixation and bone-to-implant contact (BIC) of plateau root form implants. MATERIALS AND METHODS: Implants of two different lengths (i.e., 4.5 × 11 mm long, n = 36) and 4.5 × 6 mm (short, n = 36) and two different surfaces, that is, control (AB/AE) and test (AB/AE + 300 - 500 nm bioactive ceramic coating), were placed in the proximal tibiae of six beagle dogs. The implants were retrieved for analyses 2 and 4 weeks after placement. The implants in bone specimens were subjected to torque loads until a 10% drop of the maximum torque was recorded. The specimens were evaluated under optical microscopy for bone morphology and percent BIC. Statistical analysis was performed by a generalized linear mixed effects analysis of variance model and statistical significance set at p < 0.05. RESULTS: Significantly higher torque-to-interface fracture levels for test surface groups of both lengths when compared to control surfaces were observed. No significant difference in BIC was observed between test and control implants of equal length. Histomorphological analysis showed higher degrees of bone organization between the plateaus of test implant surfaces at both implantation times. CONCLUSION: Because the presence of a thin bioactive ceramic coating on the surface did not affect BIC, but positively affected implant biomechanical fixation, the hypothesis was partially validated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".