A Retrospective Study on 287 Implants Installed in Resorbed Maxillae Grafted with Fresh Frozen Allogenous Bone
Bibliographic record
Abstract
BACKGROUND: Several studies have been performed to evaluate the clinical outcome of implants inserted into maxillae grafted with autogenous bone but few reports have focused on maxillae grafted with fresh-frozen allogenous bone (FFAB). PURPOSE: The purpose of this study is to retrospectively evaluate the clinical outcome of implants installed in resorbed maxillae augmented with FFAB. MATERIALS AND METHODS: A total of 69 patients whom had been treated with FFAB grafts to their maxillae and implant placement 4 to 6 months later were retrospectively evaluated. Edentulism was total and partial in 22 and 47 cases, respectively. A total of 287 implants of various systems had been used. A life table analysis was performed. Marginal bone loss was calculated in radiographs. RESULTS: Five of the 287 implants were lost, giving a survival rate (SVR) of 98.3% over a mean follow-up time of 26 months. The marginal bone resorption at the implants was 1.68 mm (SD = 0.44) after 1 year and 1.85 mm (SD = 0.98) after 4 years. The cumulative success rate based on defined criteria was 96% in the first year but decreased to 40% at 4 years because of marginal bone loss. The Kaplan-Meier algorithm demonstrated a better outcome for female patients, removable dentures, and total edentulism. No differences were detected among diameters, lengths, and implant site. CONCLUSION: Implants placed in FFAB showed a high SVR similar to that reported in previous studies on maxillae grafted with autogenous iliac crest bone. Although our data point to more marginal bone loss in partially edentulous patients and for fixed prosthetic restorations, the use of FFAB for reconstruction of the atrophic jaw prior to implant placement can be considered as a reliable alternative to autogenous bone.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".