The Peri‐Implantitis: Implant Surfaces, Microstructure, and Physicochemical Aspects
Bibliographic record
Abstract
There are two ways of looking at secondary failures of osseointegration; one is to reflect on possible causes for the failure, the other focuses on the pathology per se. In the first case, background factors such as mechanical trauma (adverse loading) or inflammations/infections are being discussed as the cause of failure. Then peri-implantitis is a term reserved for implant disturbance due to inflammation/infections only. However, irrespective of the original reason for the failure being adverse loading or inflammation/infection, the end result with bone resorption and inflammation may be very similar. Hence, in the present article, an alternative outlook has been chosen. Trigerring factors for peri-implantitis are generally gathered under four categories: lesions of peri-implant attachment, presence of aggressive bacteria, excessive mechanical stress, and corrosion. If only one of these factors would start a chain reaction leading to lesions, then the other factors may combine to worsen the condition. With other words, peri-implantitis is a general term dependent on a synergy of several factors, irrespective of the precise reason for first triggering off symptoms.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".