Peri‐Implant Endosseous Healing Properties of Dual Acid‐Etched Mini‐Implants with a Nanometer‐Sized Deposition of CaP: A Histological and Histomorphometric Human Study
Bibliographic record
Abstract
PURPOSE: The aim of this histological and histomorphometric study was to compare the early peri-implant endosseous healing properties of a dual acid-etched (DAE) surface (Osseotite, Implant Innovations Inc., Palm Beach Gardens, FL, USA) with a DAE surface modified with nanometer-sized calcium phosphate (CaP) particles (NanoTite, Implant Innovations Inc.) in grafted and mature maxillary bone. MATERIALS AND METHODS: Fifteen patients received two mini-implants, 1 with DAE surface (control) and 1 with a DAE + CaP surface (test), to fixate an iliac crest bone graft to the maxilla. A part of each mini-implant was in contact with the grafted bone and a part extended into the native maxillary bone. After a healing period of 3 months, the specimens were harvested and analyzed. RESULTS: Overall, a trend was seen for stronger bone response around the test mini-implants in the native bone of the maxilla. However, only the old bone particles measured by percentages of bone-to-implant contact and bone area were statistically significant (p = .025 and p = .042, respectively). CONCLUSIONS: The NanoTite surface increases the peri-implant endosseous healing properties in the native bone of the maxilla compared with the Osseotite surface, while this difference was not visible in the bone graft area. This might be a result of the lower remodeling process of the graft.
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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 | 0.001 | 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".