Acid Etching and Plasma Sterilization Fail to Improve Osseointegration of Grit Blasted Titanium Implants
Bibliographic record
Abstract
Interaction between implant surface and surrounding bone influences implant fixation. We attempted to improve the bone-implant interaction by 1) adding surface micro scale topography by acid etching, and 2) removing surface-adherent pro-inflammatory agents by plasma cleaning. Implant fixation was evaluated by implant osseointegration and biomechanical fixation.The study consisted of two paired animal sub-studies where 10 skeletally mature Labrador dogs were used. Grit blasted titanium alloy implants were inserted press fit in each proximal tibia. In the first study grit blasted implants were compared with acid etched grit blasted implants. In the second study grit blasted implants were compared with acid etched grit blasted implants that were further treated with plasma sterilization. Implant performance was evaluated by histomorphometrical investigation (tissue-to-implant contact, peri-implant tissue density) and mechanical push-out testing after four weeks observation time.Neither acid etching nor plasma sterilization of the grit blasted implants enhanced osseointegration or mechanical fixation in this press-fit canine implant model in a statistically significant manner.
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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) | 0.000 | 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".