Histomorphometrical Evaluation of Fresh Frozen Bone Allografts for Alveolar Bone Reconstruction: Preliminary Cases Comparing Femoral Head with Iliac Crest Grafts
Bibliographic record
Abstract
PURPOSE: In the past few years, the use of fresh frozen bone (FFB) grafts has significantly increased. The aim of this study was to evaluate the reconstruction of alveolar bone using femoral head and iliac crest FFB grafts. MATERIALS AND METHODS: The study included 10 patients who need endosseous implant insertion in severe atrophic maxillae. The patients were treated with FFB grafts collected from the femoral head or iliac crest. Bone regeneration was evaluated 6 months after surgery by macroscopic and microscopic analyses. RESULTS: Our results showed good regenerative capacity, both with the FFB from the femoral head and iliac crest. In particular, similar percentages of new-bone formation and graft residual were observed, whereas differences between the percentage of total bone (higher for the iliac crest) and the percentage of non-mineralized tissue (higher for the femoral head) were present. A significantly higher percentage of CD34-positive vessels in the FFB allograft from the femoral head than in the iliac crest were observed. CONCLUSIONS: These findings suggest that FFB allografts could represent a reliable option in oral and maxillofacial surgery. Nevertheless, differences between the use of femoral head or iliac crest bone allografts linked with their different structures should be considered for a more effective surgery.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".