Assessment of vertical ridge augmentation and labial prominence using buccal versus palatal approaches for maxillary segmental sandwich osteotomy (inlay technique): A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to evaluate the final vertical gain at the deficient anterior maxillary alveolar ridges using buccal versus palatal approaches for maxillary segmental sandwich osteotomy (inlay technique). This is a single-institutional randomized comparative clinical trial. MATERIAL AND METHODS: The study population was 16 patients with edentulous anterior maxillary alveolar ridges (40 implant sites). Patients were randomly divided into two equal groups. Both groups received sandwich osteotomy with down fracture of the deficient anterior maxillary alveolar ridge, using buccal approach (control group) and palatal approach (study group) with interpositional alloplastic bone blocks fixed with miniplates. Assessment included the mean percentage of vertical gain at the proposed implant sites after 4 months, taken from cross-sectional cuts of a cone beam computed tomography. RESULTS: All cases showed uneventful wound healing and a total of 40 delayed implant placement were done. Results showed that there was no statistical significance between the 2 groups in terms of bone height (P = .43) and labial prominence (P = .5) CONCLUSION: Both techniques were successful where the mean percentage of 4 months postoperative vertical bone gain of the control group was 79.9% and that of the study group was 76.5%.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".