Occlusion as a predisposing factor for peri‐implant disease: A review article
Bibliographic record
Abstract
BACKGROUND: The restoration of dental implants presents a unique challenge due to the intrinsic biomechanical differences between osseointegrated implants and natural teeth, and their subsequent responses to occlusal loading. However, controversy exists regarding the role that occlusion plays in the physiology of the peri-implant complex. PURPOSE: To provide an overview of the scientific literature regarding occlusion as it relates to implant dentistry and peri-implant disease. MATERIALS AND METHODS: This article presents a narrative review on occlusal loading and its potential effects on the peri-implant complex, as well as some generally accepted guidelines for occlusion in implant dentistry. RESULTS AND CONCLUSIONS: Although there is strong evidence linking occlusal factors to mechanical complications of dental implants, the same cannot be said regarding biological complications. There is no clear scientific evidence on the relationship between occlusal overload and peri-implant disease. However, occlusal overload may be an accelerating factor for peri-implant disease in the presence of inflammation. As the biomechanical properties of dental implants differ from that of the natural dentition, modifications to classic concepts of occlusion may be necessary when dental implants are involved. Thus, clinical recommendations are proposed which function to minimize unfavorable occlusal forces on implant restorations and reduce the associated biological and mechanical complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".