Lateral ridge augmentation with two different ratios of deproteinized bovine bone and autogenous bone: A 2‐year follow‐up of a randomized and controlled trial
Bibliographic record
Abstract
BACKGROUND: The optimal ratio of deproteinized bovine bone (DPBB) and autogenous bone (AB) for lateral augmentation is presently unknown. PURPOSE: To evaluate implant treatment outcome and radiological graft changes after lateral ridge augmentation with 2 different mixtures of DPBB and AB, 2 years after functional loading. MATERIALS AND METHODS: Thirteen patients were included in a split mouth, randomized, controlled trial. Four partially edentulous and 10 totally edentulous jaws with an alveolar ridge width of <4 mm were augmented with a graft mixture of 90:10 (DPBB:AB) on one side and 60:40 (DPBB:AB) on the contra lateral side. Graft width changes were measured on CBCT scans at different time points. Implant survival and success rates were calculated. Resonance frequency analysis and marginal bone measurements were performed after 2 years of loading. RESULTS: The survival rate was 94.4% for implants installed in the 90:10 and 100% for implants installed in the 60:40. There were no statistically significant differences in survival rate or success rate between the mixtures. The width was 5.7 mm and 6.2 mm, respectively for the 2 groups without any significant difference between the groups after 2 years of loading. There was a significant difference in graft reduction between the groups, 54.4% (90:10) and 37.5% (60:40), respectively. There were no statistically significant differences in implant stability or marginal bone levels at any time points. CONCLUSIONS: The 2 treatment modalities may be successfully used for lateral ridge augmentation and presented good clinical results after 2 years of loading. However, long-term RCTs are required before final conclusions can be provided on this specific topic.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".