Implications of considering peri‐implant bone loss a disease, a narrative review
Bibliographic record
Abstract
BACKGROUND: Peri-implantitis has been suggested to cause significant increasing proportions of implant failure with increasing time. PURPOSE: To assess whether implant failure rates in long term studies are matching the supposed high prevalence of peri-implantitis. MATERIAL AND METHODS: This paper is written as a narrative review of the long-term clinical investigations available in the literature. RESULTS: Some implant systems have seen unacceptable marginal bone loss figures with time coupled to increased implant failure rates, resulting in the withdrawal of these systems. The reasons for such mishap are generally unknown, with the exception of one system failure that was found to be due to improper clinical handling. Modern, moderately rough implant systems have functioned excellently over 10-15 years of follow up with minor problems with marginal bone loss and implant failure rates within a few per cent. Machined implants have functioned adequately over 20-30 years of follow up. Implant failures occur predominantly during the first few years after implant placement. No significant increase of implant failures has been observed thereafter over 20-30 years of follow up. Over the years of our new millennium, scientific and technical advances have allowed the discovery of numerous molecular pathways and cellular interactions between the skeletal and immune system promoting the development of the interdisciplinary field called osteoimmunology. Nowadays, this knowledge has not only allowed the emergence of new etiologic paradigms for bone disease but also a new dynamic approach on the concept of osseointegration and MBL around oral implants, re-evaluating our older disease oriented outlook. This facilitates at the same time the emergence of translational applications with immunological perspectives, scientific approaches based on omics sciences, and the beginning of an era of personalized dental implant therapy to improve the prognosis of oral implant treatment. CONCLUSIONS: Oral implant systems have been found to function with very good clinical outcome over follow-up times of 20-30 years. Registered implant failures have occurred predominantly during the first few years after implantation, and there has been no significant increase in late failures due to peri-implantitis.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".