Bioactive treatments in bone grafts for implant‐based rehabilitation: Systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The use of bioactive proteins, such as rhBMP-2, may improve bone regeneration in oral and maxillofacial surgery. PURPOSE: Analyze the effect of using bioactive proteins for bone regeneration in implant-based rehabilitation. MATERIALS AND METHODS: Seven databases were screened. Only clinical trials that evaluated the use of heterologous sources of bioactive proteins for bone formation prior to implant-based rehabilitation were included. Statistical analyses were carried out using a random-effects model by comparing the standardized mean difference between groups for bone formation, and risk ratio for implant survival (P ≤ .05). RESULTS: Seventeen studies were included in the qualitative analysis, and 16 in the meta-analysis. For sinus floor augmentation, bone grafts showed higher amounts of residual bone graft particles than bioactive treatments (P ≤ .05). While for alveolar ridge augmentation bioactive treatments showed a higher level of bone formation than control groups (P ≤ .05). At 3 years of follow-up, no statistically significant differences were observed for implant survival (P > .05). CONCLUSIONS: Bioactive proteins may improve bone formation in alveolar ridge augmentation, and reduce residual bone grafts in sinus floor augmentation. Further studies are needed to evaluate the long-term effect of using bioactive treatments for implant-based rehabilitation.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".