Long‐term survival and success rate of dental implants placed in reconstructed areas with extraoral autogenous bone grafts: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the long-term survival and success rates of implants placed in reconstructed areas using microvascularized or non-microvascularized extraoral bone grafts. MATERIALS AND METHODS: An electronic search was performed in five databases and in gray literature for articles published until June, 2023. The eligibility criteria comprised observational studies (prospective or retrospective) and clinical trials, reporting survival and success rates of implants placed in extraoral bone grafts. A meta-analysis (implant failure) was categorized into subgroups based on the type of bone graft used. The risk of bias within studies was assessed using the Newcastle-Ottawa Scale. RESULTS: Thirty-one studies met the inclusion criteria. The mean follow-up time was 92 months. The summary estimate of survival rate at the implant level were 94.9% (CI: 90.1%-97.4%) for non-vascularized iliac graft, 96.5% (CI: 91.4%-98.6%) for non-vascularized calvaria graft, and 92.3% (CI: 89.1%-94.6%) for vascularized fibula graft. The mean success rate and marginal bone loss (MBL) were 83.2%; 2.25 mm, 92.2%; 0.93 mm, and 87.6%; 1.49 mm, respectively. CONCLUSIONS: Implants placed in areas reconstructed using extraoral autogenous bone graft have high long-term survival rates and low long-term MBLs. The data did not demonstrate clinically relevant differences in the survival, success, or MBL of grafts from different donor areas or with different vascularization. This systematic review was registered in INPLASY under number INPLASY202390004.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".