Sandwich osteotomy with interpositional grafts for vertical augmentation of the mandible
Bibliographic record
Abstract
Sandwich osteotomy is a technique for vertical augmentation based on the principle of a graft being placed between two pedicled native bones. The inherent vascularization helps in graft consolidation. The aim is to review the bone height gained, implant survival and pitfalls with sandwich osteotomy. The PICO model was used to identify the suitable studies for the review. Oxford level of evidence, Newcastle Ottawa Scale and Cochrane's tool for Systematic Reviews of Interventions was applied for identifying study quality. Meta-analysis was performed with the help of RevMan. Funnel plot was used to evaluate publication bias and bias during article selection. Difference in means was used as principal summary measure. Fixed effects model with inverse variance statistics was used. I2test statistics was applied to identify study heterogeneity. Forest plots were produced for the outcome variables with 95% confidence interval (CI) and overall treatment effects and subgroup effects at a significance level of 0.05. The overall implant survival rate ranged from 90%-100% and prosthetic survival rate from 87%-95%. An overall 6-10mm of bone can be gained in the anterior mandible and 4-8mm in the posterior mandible. A total of 1030 implants were placed of which 988 implants survived after the mean follow up periods (odds ratio: 0.77, 95% CI: 0.49-1.21). Implant survival is independent of the graft being used. Vertical augmentation in the posterior mandible is limited compared to anterior owing to the presence of inferior alveolar nerve and the keratinized tissue deficiency.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".