Impact of local predisposing/precipitating factors and systemic drivers on peri‐implant diseases
Bibliographic record
Abstract
BACKGROUND: Strong evidence suggests the infectious nature of peri-implant diseases occurring in susceptible hosts. Epidemiological reports, though, indicate that peri-implantitis is a site-specific entity. Hence, the significance of local factors that may predispose/precipitate plaque accumulation and the impact of systemic drivers that alter the immune response are relevant in the prevention and management of peri-implant disorders. PURPOSE: The purpose of the present review is to shed light on the significance of local and systemic factors on peri-implant diseases, making special emphasis on the associations with peri-implantitis. METHODS: The biologic plausibility and supporting evidence aiming at providing a concluding remark were explored in the recent scientific literature for local predisposing/precipitating factors and systemic drivers related to peri-implant diseases. RESULTS: Local predisposing factors such as soft tissue characteristics, implant position and prosthetic design proved being strongly associated with the occurrence of peri-implant diseases. Hard tissue characteristics, however, failed to demonstrate having a direct association with peri-implant diseases. Robust data points toward the strong link between residual sub-mucosal cement and peri-implant diseases, while limited data suggests the impact of residual sub-mucosal floss and peri-implantitis. Systemic drivers/habits such as hyperglycemia and smoking showed a strong negative impact on peri-implantitis. However, there is insufficient evidence to claim for any link between metabolic syndrome, atherosclerotic cardiovascular disease, and obesity and peri-implant diseases. CONCLUSION: Local predisposing/precipitating factors and systemic drivers may increase the risk of peri-implant diseases. Therefore, comprehensive anamnesis of the patients, educational/motivational programs and exhaustive prosthetically-driven treatment planning must be fostered aiming at reducing the rate of biological complications in implant dentistry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".