Titanium Granules for Augmentation of the Maxillary Sinus – A Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Biomaterials are commonly used to augment the maxillary sinus floor prior to or in conjunction with dental implant installation. Recently, porous titanium granules (PTGs) have been used in oral implant surgery to stabilize implants and function as an osteoconductive matrix. PURPOSE: To evaluate if PTGs can be safely used in a larger population of patients, treated by different surgeons, when sinus floor augmentation was required in conjunction with implant installation. The primary endpoint was 12-month survival rate of the dental implants. Biopsies for histology were taken from the augmented area. MATERIALS AND METHODS: At five centers, 40 subjects with uni or bilateral posterior edentulism and atrophy of the posterior maxilla (3-6 mm) were enrolled. In a single-stage procedure, PTG and one to three dental implants were installed in each quadrant. In total, 70 implants were included in the study. RESULTS: One immobile implant was removed. The mean marginal bone loss was 0.5 mm and 0.8 mm, on the mesial and distal side, respectively. Histologically, all biopsies demonstrated bone ingrowth. CONCLUSIONS: The results suggest that PTG can be safely and effectively used as augmentation material in the sinus floor when used with dental implants in a one-stage procedure.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".