Osteotome Sinus Floor Elevation and Simultaneous Placement of Implants – A 1‐Year Retrospective Study with Astra Tech Implants
Bibliographic record
Abstract
BACKGROUND: The bone support for implants in the posterior part of the maxilla is often poor. This condition may be treated with augmentation of the maxillary sinus floor. The most common technique used is to elevate the sinus floor by inserting a bone graft through a window opened in the lateral antral wall, although less invasive techniques with osteotomes have been used since 1994. PURPOSE: The aim of this study was to evaluate the clinical and radiographic outcome of implants placed in the posterior maxilla with the osteotome sinus floor elevation (OSFE) technique without grafting. MATERIALS AND METHODS: The study population comprised 36 consecutive patients in whom 53 implants were inserted with the OSFE technique. The indication for sinus floor elevation was that the bone height below the maxillary sinus was considered to be 10 mm or less. RESULTS: The mean height of the alveolar process in the intended implant sites was 6.3 +/- 0.3 mm, and the mean elevation of the sinus floor was 4.4 +/- 0.2 mm. At the 1-year follow-up, two implants had been lost, both in edentulous patients. The remaining 51 implants inserted were in function, giving a 1-year cumulative survival rate of 96%. Implants used in single-tooth replacements and in partially edentulous cases had a 100% survival rate. The mean marginal bone level at the time of loading of the implants was 0.1 +/- 0.04 mm below the reference point. One year later, the corresponding value was 0.5 +/- 0.06 mm. The mean bone loss between the two examinations was 0.4 +/- 0.05 mm. CONCLUSIONS: The OSFE technique, without bone grafts, was found to produce predictable results in the treatment of 36 patients with restricted bone volume in the posterior part of the maxilla.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".