Risk factors impacting the survival of implants replaced following failure: A retrospective study
Bibliographic record
Abstract
AIM: This study aimed to investigate factors influencing the survival of replaced dental implants. MATERIALS AND METHODS: Charts from 2005 to 2021 were reviewed. Replaced implants after removal for the first time were identified. Depending on their survival, the replaced group was divided into the surviving and second-removal groups. Risk factors affecting survival of replaced implants were evaluated considering clustering of multiple implants within patients. RESULTS: The present study included 464 replaced implants of 370 patients, of which 429 and 35 implants were categorized into the surviving group and the second-removal group. The 5-year survival rate was 90.2 ± 0.18% in replaced implants at sites with a periodontitis history and 97.0 ± 0.15% at sites without a periodontitis history (p = 0.008). The 5-year survival rate was 89.1 ± 0.27% in replaced implants with guided bone regeneration (GBR) at first implant placement and 93.9 ± 0.14% at non-GBR (p = 0.032). The 5-year survival rate was 97.6 ± 0.13% in replaced implants with GBR and 90.3 ± 0.17% in replaced implants without GBR (p = 0.026). In the multivariable analysis adjusted for clinical variables, periodontitis history (adjusted hazard ratio [aHR] = 3.417; 95% confidence interval [CI] = 1.161-10.055), GBR at first implant placement (aHR = 2.152; 95% CI = 1.052-4.397) and non-GBR at primary implant replacement (aHR = 0.262; 95% CI = 0.088-0.778) were identified as independent risk factors for second implant removal. CONCLUSIONS: Periodontitis history, GBR at first implant placement and non-GBR at primary implant replacement were identified as risk factors affecting the survival of replaced implants.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".