Bibliographic record
Abstract
BACKGROUND: A consensus meeting was arranged to critically analyze whether the high figures of peri-implantitis at machined implants that recently have been reported in the literature are valid also for modern implants. PURPOSE: The aims of this paper were to present the outcomes from the consensus meeting and to evaluate recent long-term clinical studies on modern implants with regard to frequency of peri-implant infection. MATERIALS AND METHODS: Ten different studies of three modern implant brands of moderately rough surfaces with 10-year or longer follow-up times were found through a PubMed and manual search. RESULTS: It was concluded that bleeding on probing or probing depths are weak indicators of crestal bone loss (CBL); that CBL occurs for many other reasons than infection; that implant-, clinician-, and patient-related factors contribute to CBL; and that modern oral implants outperform older devices. Based on a literature search, the frequency of implants with reported peri-implant infection and significant bone loss leading to implant removal or other surgical intervention was on average 2.7% during 7 to 16 years of function. CONCLUSION: The summed frequency of peri-implantitis and implant failure is commonly less than 5% over 10 years of follow-up for modern implants when using established protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".