Comparative analysis of two biomaterials mixed with autogenous bone graft for vertical ridge augmentation: A histomorphometric study in humans
Bibliographic record
Abstract
BACKGROUND: The vertical increase of the alveolar ridge dimension using allograft or xenograft mixed with autogenous bone graft and covered by a nonabsorbable high-density polytetrafluoroethylene (d-PTFE) membrane is well documented in the literature. PURPOSE: The aim of this study was to assess vital mineralized tissue formation in vertical ridge augmentation (VRA) procedures using autogenous bone chips mixed either with an allograft or a xenograft. METHODS: This prospective clinical trial recruited 16 partially edentulous patients to undergo vertical ridge augmentation in one or more sites, making up a total of 24 samples for histological evaluation. Patients were sequentially stratified into Group A (treated with a freeze-dried bone allograft [FDBA] mixed with autogenous bone) or to Group B (treated with a bovine xenograft mixed with autogenous bone). Histological samples were analyzed according to the biomaterial used for VRA. Histological samples were obtained on the same day of membrane removal and implant placement. RESULTS: Thirty-three implants were placed in 16 sites of regenerated bone via VRA, 13 patients with ridge augmentation in the posterior mandible, and 3 patients with VRA in the anterior maxilla. Group A (FDBA + autogenous) and Group B (xenograft + autogenous) showed a percent vital mineralized tissue (VMT) area of 67.64 ± 16.84 and 60.93 ± 18.25, respectively. A significant difference between the two biomaterials was not observed. CONCLUSION: When mixed with autogenous bone, either allografts or xenografts may provide a successful augmentation. Either mixture could serve as reliable alternative in VRA for obtaining a high percentage of VMT.
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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 | 0.001 | 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".