Time-Dependent Changes in Fresh-Frozen Bone Block Grafts: Tomographic, Histologic, and Histomorphometric Findings
Bibliographic record
Abstract
BACKGROUND: Bone allografts have shown satisfactory clinical results in alveolar ridge reconstructions. However, the process of incorporation and the resorption rates of these grafts are not yet fully understood. PURPOSE: The aim of this study was to use computed tomography (CT), histology, and histomorphometry to assess the time-dependent rates of resorption and incorporation of fresh-frozen bone allografts. MATERIALS AND METHODS: Twenty-four patients underwent alveolar ridge reconstruction with bone block allografts and were randomly allocated to three groups with different graft healing periods (4, 6, or 8 months) before implant placement. To assess the resorption rates, CT scans were acquired within 7 days after bone graft surgery and at the end of the period. Graft samples were collected and sent for histological and histomorphometric analyses. RESULTS: The graft resorption mean rates were 50.78% ± 10.43, 32.77% ± 7.84, and 13.02% ± 3.86 for the 4-, 6-, and 8-month groups, respectively, and were significantly different among the three groups. Newly formed bone with osteocytes near the grafted bone was observed in all three groups. The number of osteocytes was significantly lower at 4 months. Grafted bone remains were significantly higher in the shortest period of time. All of the grafts showed large amounts of calcified tissue. CONCLUSIONS: All three groups showed new bone formation and different bone resorption rates. Graft healing periods of 4 months showed less graft resorption and seemed to be the most favorable for implant placement. Healing periods of 8 months showed the largest rate of graft resorption, which could render the grafts unfavorable for implant placement.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".