Bibliographic record
Abstract
If dental implantology is an increasingly successful treatment modality, why should we still need to understand the mechanisms of peri-implant bone healing? Are there differences in cortical and trabecular healing? What does "poor quality" bone mean? What stages of healing are most important? How do calcium phosphate-coated implants accelerate healing? What is the mechanism of bone bonding? While there are still many aspects of peri-implant healing that need to be elucidated, it is now possible to deconvolute this biological reaction cascade, both phenomenologically and experimentally, into three distinct phases that mirror the evolution of bone into an exquisite tissue capable of regeneration. The first and most important healing phase, osteoconduction, relies on the recruitment and migration of osteogenic cells to the implant surface, through the residue of the peri-implant blood clot. Among the most important aspects of osteoconduction are the knock-on effects generated at the implant surface, by the initiation of platelet activation, which result in directed osteogenic cell migration. The second healing phase, de novo bone formation, results in a mineralized interfacial matrix equivalent to that seen in the cement line in natural bone tissue. These two healing phases, osteoconduction and de novo bone formation, result in contact osteogenesis and, given an appropriate implant surface, bone bonding. The third healing phase, bone remodeling, relies on slower processes and is not considered here. This discussion paper argues that it is the very success of dental implants that is driving their increased use in ever more challenging clinical situations and that many of the most important steps in the peri-implant healing cascade are profoundly influenced by implant surface microtopography. By understanding what is important in peri-implant bone healing, we are now able to answer all the questions listed above.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".