Clinical Consequences of IL-1 Genotype on Early Implant Failures in Patients under Periodontal Maintenance
Bibliographic record
Abstract
BACKGROUND: Implant failure and biologic complications such as periimplantitis are not completely avoidable. Are there any genetic and microbiologic parameters that could be used to identify patients at risk for implant failure, preferably prior to treatment? This would result in improvement of the diagnostics, treatment decision, and risk assessment. PURPOSE: The aims of this retrospective study were to describe (1) the absolute failure rate of Brånemark System implants (Nobel Biocare AB, Göteborg, Sweden) consecutively installed over a 10-year period in partially edentulous patients treated for periodontal disease prior to implant treatment and under regular professional maintenance, (2) the rate of interleukin-1 (IL-1) polymorphism in those patients who experienced at least one implant failure during the first year of function, and (3) the prevalence of periodontal pathogens in dental and periimplant sites with and without signs of inflammation. MATERIAL AND METHODS: Of 766 patients, 81 encountered at least one implant failure; 22 patients were clinically examined and were tested genetically for IL-1 genotypes. The presence of Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis, and Prevotella nigrescens was analyzed. RESULTS: The absolute implant survival rate for the whole population was 95.32%; 10.57% of the patients encountered an implant loss. Implant loss in the examined group (n = 22) was 32 of 106 (30.1%); 10 (45%) of the 22 patients were smokers, and 6 (27%) of the 22 patients were IL-1 genotype positive. Patients positive for IL-1 genotype were not more prone to implant loss; however, a significant synergistic effect with smoking was demonstrated. Between patients who were IL-1 genotype positive and those who were IL-1 genotype negative, the differences in regard to bleeding on probing or periodontal pathogens did not reach statistical significance. CONCLUSION: The overall implant failure rate in a population treated and maintained for periodontal disease is similar to that of healthy subjects. A synergistic effect found between smoking and a positive IL-1 genotype resulted in a significantly higher implant loss. This indicates that further research with a larger patient group should focus on multifactorial analysis for adequate risk assessment.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".