Histologic Evaluation of a Stem Cell–Based Sinus‐Augmentation Procedure
Bibliographic record
Abstract
BACKGROUND: Predictability has been demonstrated for the long-term success of dental implants placed simultaneously with or after a sinus-augmentation procedure. However, the time required to obtain optimal bone formation can be from 6 to 9 months or longer with grafting materials other than autogenous bone. For this reason, there is interest in a surgical technique that does not require the harvest of autogenous bone but still results in sufficient bone formation within a relatively short time frame. METHODS: The purpose of this case series was to evaluate the bone formation following sinus-augmentation procedures using an allograft cellular bone matrix containing native mesenchymal stem cells. Biopsy and histologic evaluation were performed after approximately 4 months of healing. RESULTS: Histomorphometric analysis revealed an average vital bone content of 33% (range, 22% to 40%) and an average residual graft content of 6% (range, 3% to 7%) for the five cases reported that had an average healing period of 4.1 months (range, 3 to 4.75 months). CONCLUSION: The high percentage of vital bone content, after a relatively short healing phase, may encourage a more rapid initiation of implant placement or restoration when a cellular grafting approach is considered.
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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 | 0.000 |
| 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) | 0.003 | 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".