Implant survival rate in calvarial bone grafts: A retrospective clinical study with 10 year follow‐up
Bibliographic record
Abstract
BACKGROUND: In this study, we present medium- and long-term data on implant survival in a cohort of patients with severe maxillary atrophy rehabilitated using reconstructive implant site development with calvarial bone grafts. MATERIALS AND METHODS: We obtained clinical records from patients treated with implant rehabilitation supported by calvaria bone grafts in the Oral Surgery Department of IRCSS San Raffaele (Milan, Italy). Implant and prosthetic survival and success rates were retrospectively evaluated. Graft survival and postoperative complications were also assessed. RESULTS: A total of 207 implants placed in 32 patients were evaluated for a mean period of 10.0 years from loading. After 10 years, the cumulative survival rate was 97.10%, the implant success rate was 92.75%, and the prosthetic complication rate was 9.76%. A graft survival percentage of 96.88% was observed, and postoperative complications occurred in 28.13% of cases. CONCLUSIONS: The 10-year survival rate and prosthetic complications for patients treated with implant rehabilitation supported by calvarial bone grafts are excellent, as implant loss was relatively rare, although limited subjects were available for the 10-year follow-up.
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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 | 0.002 |
| 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".