A Human Clinical, Histological, Histomorphometrical, and Radiographical Study on Biphasic<scp>HA</scp>‐Beta‐<scp>TCP</scp>30/70 in Maxillary Sinus Augmentation
Bibliographic record
Abstract
BACKGROUND: By mixing hydroxyapatite (HA) and tricalcium phosphate (TCP), biphasic calcium phosphate ceramics can be obtained, and by varying their ratio it is possible to tailor the characteristics of the biomaterial. PURPOSE: The aim of the present human study was to evaluate the histological and radiographical aspects of bone formation in maxillary sinus augmentation using a 30/70 HA-beta-TCP with a reticular structure. MATERIALS AND METHODS: A total of 12 patients, undergoing two-stage sinus augmentation procedure using HA-beta-TCP at a ratio of 30/70, were included in the present study. After a 6-month healing period, during implant insertion, radiographical analysis was performed, and then the bone core biopsies were harvested and processed for histology. RESULTS: At radiographic evaluation, the bone gain was on average 6.85 ± 0.60 mm. HA-beta-TCP 30/70 appeared to be lined by newly formed bone, with no gaps at the interface. The histomorphometric analysis revealed 26 ± 2% of residual grafted biomaterial, 29 ± 3% of newly formed bone, and 45 ± 2% of marrow spaces. CONCLUSIONS: The present results indicate histologically the high biocompatibility and osteoconductivity of HA-beta-TCP 30/70, and clinically its successful use for sinus augmentation procedures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".