Analysis of implant loss risk factors after simultaneous guided bone regeneration: A retrospective study of 5404 dental implants
Bibliographic record
Abstract
PURPOSE: The purpose was to analyze the risk factors for implant loss after simultaneous guided bone regeneration (GBR). MATERIALS AND METHODS: Patients who underwent implant placement with simultaneous GBR between January 2011 and December 2018 were screened for this study. The cumulative survival rate (CSR) was calculated using the life table method. Log-rank test and Kaplan-Meier survival estimates were used to identify potential risk factors for implant loss. The association between the investigated variables and implant loss was determined using hazard ratios (HRs) obtained from a multivariate Cox regression analysis. RESULTS: A total of 3973 patients with 5404 implants were included in this study. The CSRs of the implants at 1, 5, and 10 years were 99.6%, 98.9%, and 98.7%, respectively. Male patient (HR = 2.94, 95% CI: 1.41-6.14), periodontitis (HR = 4.26, 95% CI: 2.05-9.86), tissue-level implants (HR = 3.02, 95% CI: 1.30-6.98), narrow implants (HR = 2.71, 95% CI: 1.12-6.57), and implant length ≤10 mm (HR = 2.91, 95% CI: 1.41-6.02) significantly increased the risk of implant loss (p < 0.05). The risk of implant loss was significantly higher in the maxillary posterior region (HR = 2.26, 95% CI: 1.04-4.90) than in the maxillary anterior region (p < 0.05). Compared to Straumann, Nobel (HR = 4.07, 95% CI: 1.75-9.44) and other implant systems (HR = 14.23, 95% CI: 4.32-46.85) showed a significantly higher risk of implant loss (p < 0.05). CONCLUSION: Male patient, periodontitis, maxillary posterior region, Nobel implant system, other implant systems, tissue-level implants, narrow implants, and implant length ≤10 mm were considered risk factors for implant loss after simultaneous GBR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".