Assessing new bone formation after maxillary sinus augmentation surgery using right anterior iliac crest bone marrow aspirate and cancellous allograft
Bibliographic record
Abstract
The aim of this retrospective study was to assess the change in the alveolar bone height following sinus augmentation surgery utilising a combination of right anterior iliac crest bone marrow aspirates and allogeneic cancellous bone, as observed through cone-beam computed tomography (CBCT). A total of 46 patients who underwent CBCT scans taken before and after sinus augmentation surgery were evaluated. The alveolar bone heights were compared over time. Average and frequency measures, as well as a Student's t test were performed to determine significant differences between the graft height between the preoperative and postoperative alveolar bone heights. Overall, a statistically significant increase in alveolar bone heights from a mean of 5.44 mm ± 2.9 m to 12.49 mm ± 4.8 m was observed on the postoperative CBCT scan. The mean postoperative CBCT was 23 months (range: 5-59), p < 0.01. In conclusion, the use of bone marrow aspirates from the right anterior iliac crest combined with allogeneic cancellous bone materials can be successfully used to create increased bone height for pre-implant site grafting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".