Bone vitality and vascularization of mandibular and maxillary bone grafts in maxillary sinus floor elevation: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Mandibular retromolar (predominantly cortical) and maxillary tuberosity (predominantly cancellous) bone grafts are used in patients undergoing maxillary sinus floor elevation (MSFE) for dental implant placement. The aim of this retrospective cohort study was to investigate whether differences exist in bone formation and vascularization after grafting with either bone source in patients undergoing MSFE. METHODS: Fifteen patients undergoing MSFE were treated with retromolar (n = 9) or tuberosity (n = 6) bone grafts. Biopsies were taken 4 months postoperatively prior to dental implant placement, and histomorphometrically analyzed to quantify bone and osteoid area, number of total, apoptotic, and receptor activator of nuclear factor-κB ligand (RANKL)-positive osteocytes, small and large-sized blood vessels, and osteoclasts. The grafted area was divided in three regions (caudal-cranial): RI, RII, and RIII. RESULTS: Bone volume was 40% (RII, RIII) higher and osteoid volume 10% (RII) lower in retromolar compared to tuberosity-grafted areas. Total osteocyte number and number of RANKL-positive osteocytes were 23% (RII) and 90% (RI, RII) lower, but osteoclast number was higher (retromolar: 12, tuberosity: 0) in retromolar-grafted areas. The total number of blood vessels was 80% (RI) to 60% (RIII) lower, while the percentage of large-sized blood vessels was 86% (RI) to 25% (RIII) higher in retromolar-grafted areas. Number of osteocyte lacunae and apoptotic osteocytes were similar in both bone grafts used. CONCLUSIONS: Compared to the retromolar bone, tuberosity bone showed increased bone vitality and vascularization in patients undergoing MSFE, likely due to faster bone remodeling or earlier start of new bone formation. Therefore, tuberosity bone grafts might perform better in enhancing bone regeneration.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".