On osseointegration in relation to implant surfaces
Bibliographic record
Abstract
BACKGROUND: The understanding of mechanisms of osseointegration as well as applied knowledge about oral implant surfaces are of paramount importance for successful clinical results. PURPOSE: The aim of the present article is to present an overview of osseointegration mechanisms and an introduction to surface innovations with relevance for osseointegration that will be published in the same supplement of Clinical Implant Dentistry and Related Research. MATERIALS AND METHODS: The present article is a narrative review of some osseointegration and implant surface-related details. RESULTS AND CONCLUSIONS: Osseointegration has a changed definition since it is realized today that oral implants are but foreign bodies and that this fact explains osseointegration as a protection mechanism of the tissues. Given adequate stability, bone tissue is formed around titanium implants to shield them from the tissues. Oral implant surfaces may be characterized by microroughness and nanoroughness, by surface chemical composition and by physical and mechanical parameters. An isotropic, moderately rough implant surface such as seen on the TiUnite device has displayed improved clinical results compared to previously used minimally rough or rough surfaces. However, there is a lack of clinical evidence supporting any particular type of nanoroughness pattern that, at best, is documented with results from animal studies. It is possible, but as yet unproven, that clinical results may be supported by a certain chemical composition of the implant surface. The same can be said with respect to hydrophilicity of implant surfaces; positive animal data may suggest some promise, but there is a lack of clinical evidence that hydrophilic implants result in improved clinical outcome of more hydrophobic surfaces. With respect to mechanical properties, it seems obvious that those must be encompassing the loading of oral implants, but we need more research on the mechanically ideal implant surface from a clinical aspect.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".