Deposition of nanometer scaled calcium‐phosphate crystals to implants with a dual acid‐etched surface does not improve early tissue integration
Bibliographic record
Abstract
OBJECTIVE: To evaluate hard and soft tissue healing to implants with a dual acid-etched surface with and without deposition of calcium-phosphate crystals. MATERIALS AND METHODS: Three months after extraction of mandibular premolars in six Labrador dogs, four osteotomy preparations, 8 mm deep and 3 mm wide, were performed. The prepared canals were widened in the marginal 4 mm zone to 3.74 mm. Implants with an 8 mm long and 3.75 mm wide intraosseous portion and a 5.0 mm high and 4.0 mm wide transmucosal part were placed in such a way that the base of the wider neck-portion of the implant coincided with the crestal bone. The implants were dual acid - etched (Osseotite(®); Biomet 3i). The surface of the test implants was, in addition, modified by a discrete deposition of calcium-phosphate crystals (Nanotite™; Biomet 3i). Every second implant placed was a test unit. After 2 weeks the implant installation procedure was repeated in the opposite side of the mandible. Two weeks later the animals were euthanized and biopsies were obtained and prepared for histological analysis. RESULTS: The degree of bone-to implant contact (BIC%) was larger at implants without (Osseotite) than in those with (Nanotite) calcium-phosphate crystals. No differences were found regarding soft tissue dimensions and composition between the two types of implants. CONCLUSION: It is suggested that deposition of nanometer-sized calcium-phosphate crystals to implants with a dual acid-etched surface does not improve early tissue integration.
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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 | 0.000 | 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 | 0.001 | 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".