An Exploratory Case‐Control Study on the Impact of <scp><i>IL</i></scp><i>‐1</i> Gene Polymorphisms on Early Implant Failure
Bibliographic record
Abstract
BACKGROUND: The association between IL-1 gene polymorphisms and peri-implantitis has been well documented. However, data on the association with early implant failure are scarce. PURPOSE: The objective of this case-control study was to explore the impact of IL-1A (-889), IL-1B (-511), and IL-1B (+3,954) gene polymorphisms on early implant failure in Caucasians. MATERIALS AND METHODS: Between September 2004 and August 2007, 461 patients were treated with dental implants at the University Hospital in Ghent, Belgium. Fourteen subjects of this patient group who had experienced one or more early implant failures (within 6 months from implant installation) were recruited as "cases." Fourteen "controls," matched in terms of age, gender, and smoking habits, with only surviving implants, were selected from the same patient group. Allele and genotype analysis was performed on the basis of a blood sample by Sanger sequencing of polymerase chain reaction products containing the IL-1A (-889), IL-1B (-511), and IL-1B (+3,954) gene polymorphisms. RESULTS: A significant impact of the IL-1A (-889) T allele (p = .039) and the IL-1B (+3,954) T allele (p = .003) on early implant failure was demonstrated (odds ratios = 3.9 and 15.0, respectively). In addition, the genotypic distribution differed significantly between cases and controls for IL-1B (+3,954) (p = .015). CONCLUSIONS: The IL-1B (+3,954) gene polymorphism seems to affect osseointegration. Additional case-control studies in larger patient groups are needed to confirm this observation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".