Human Studies of Vertical and Horizontal Alveolar Ridge Augmentation Comparing Different Types of Bone Graft Materials: A Systematic Review
Bibliographic record
Abstract
Alveolar ridge augmentation can be completed with various types of bone augmentation materials (autogenous, allograft, xenograft, and alloplast). Currently, autogenous bone is labeled as the "gold standard" because of faster healing times and integration between native and foreign bone. No systematic review has currently determined whether there is a difference in implant success between various bone augmentation materials. The purpose of this article was to systematically review comparative human studies of vertical and horizontal alveolar ridge augmentation comparing different types of bone graft materials (autogenous, allograft, xenograft, and alloplast). A MEDLINE search was conducted under the 3 search concepts of bone augmentation, dental implants, and alveolar ridge augmentation. Studies pertaining to socket grafts or sinus lifts were excluded. Case reports, small case series, and review papers were excluded. A bias assessment tool was applied to the final articles. Overall, 219 articles resulted from the initial search, and 9 articles were included for final analysis. There were no discernible differences in implant success between bone augmentation materials. Generally, patients preferred nonautogenous bone sources as there were fewer hospital days, less pain, and better recovery time. Two articles had industrial support; however, conclusions of whether that support influenced the outcomes could not be determined. Future comparative studies should compare nonautogenous bone sources and have longer follow-up times.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".