Anterior maxillary sandwich osteotomy technique with simultaneous implant placement: A novel approach for management of vertical deficiency
Bibliographic record
Abstract
BACKGROUND: The introduction of sandwich osteotomy technique with simultaneous implant placement allowed various procedures to be carried out with a level of great precision and accuracy thus saving time for the patient and clinician. PURPOSE: The aim of the current study is to evaluate the efficacy of this new technique regarding increasing the anterior maxillary alveolar height with simultaneous implant placement. MATERIALS AND METHODS: Nine patients suffering from multiple missing anterior maxillary teeth were selected with vertical dimension not less than 10 mm. anterior maxillary sandwich osteotomy technique was carried out for all patients using xenograft bone particulate with simultaneous implant placement at single stage surgery. RESULTS: For two patients, four implants showed significant marginal bone loss with maximum marginal bone loss up to 2.8 mm. However, the immediate postoperative follow up went uneventful for all nine patients included in the present study. None of them showed any complication regarding postoperative wound dehiscence, infection, or segment mobility. Four months postoperative upon the prosthetic phase, all the 18 placed implant were clinically osseointegtated. CONCLUSION: All 18 implants were successfully integrated in the present study. The prosthetic phase started after 4 months for all cases and there was no need for harvesting of autogenous bone from the patient. But further studies are required to evaluate the viability of such approach in single implant placement cases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".