Vertical Bone Gain Post‐Sinus Lifting and Simultaneous Implant Placement With Osseodensification: A Retrospective Study
Bibliographic record
Abstract
OBJECTIVE: To determine the bone height gain (BHG) achieved after sinus floor elevation (SFE) with osseodensification (OD). MATERIALS AND METHODS: Patients from an implantology learning center presenting one missing teeth in the posterior maxilla and insufficient residual bone height (RBH) were included. SFE with simultaneous implant placement was performed using Densah drills. Demineralized bovine bone mineral, hydroxyapatite+β-TCP, calcium phosphosilicate, and autologous leukoplakelet fibrin were used as graft biomaterials. BHG was obtained by subtracting the implant length from the initial bone height. RESULTS: Sinus membrane perforation occurred in 4.8% of 144 cases. One hundred and thirty-seven patients were analyzed for BHG. RBH equaled 5.4 ± 1.8 mm, with 42 (30.7%) cases having < 5 mm. The average implant length (AIL) was 8.8 ± 1.1 mm, resulting in a mean BHG of 3.4 ± 1.7 mm. BHG was significantly higher in cases with RBH < 5 mm (5.23 ± 1.45 mm) than ≥ 5 mm (2.62 ± 1.15 mm) (p < 0.001). Greater gains were observed in first molars (3.70 ± 1.72). Implant brand and graft type did not influence BHG. The survival rate of the implants reached 97% after 6 months of osseointegration. CONCLUSIONS: OD with simultaneous implant placement was effective for SFE, applying a variety of implant brands and type of bone substitute, resulting in clinically relevant BHG, adequate AIL, and low complication rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".