High‐Resolution Three‐Dimensional Computed Tomography Analysis of the Clinical Efficacy of Cultured Autogenous Periosteal Cells in Sinus Lift Bone Grafting
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Sinus lift (SL) using cultured autogenous periosteal cells (CAPCs) combined with autogenous bone and platelet-rich plasma (PRP) was performed to evaluate the effect of cell administration on bone regeneration, by using high-resolution three-dimensional computed tomography (CT). MATERIALS AND METHODS: SL with autogenous bone and PRP plus CAPC [CAPC(+)SL] was performed in 23 patients. A piece of periosteum taken from the mandible was cultured in M199 medium with 10% fetal bovine serum (FBS) for 6 weeks. As control, 16 patients received SL with autogenous bone and PRP [CAPC(-)SL]. Three-dimensional CT imaging was performed before and 4 months and 1 year after SL, and stratification was performed based on CT numbers (HUs) corresponding to soft tissue and cancellous or cortical bone. RESULTS: The augmented bone in CAPC(+)SL revealed an increase in HUs corresponding to cancellous bone as well as a decrease in HUs corresponding to grafted cortical bone. In addition, HUs corresponding to cancellous bone in the graft bed were increased in CAPC(+)SL but were decreased in CAPC(-)SL. Insertion torque during implant placement was significantly higher in CAPC(+)SL. CONCLUSION: By promoting bone anabolic activity both in augmented bone and graft bed, CAPCs are expected to aid primary fixation and osseointegration of implants in clinical applications.
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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 | 0.000 |
| 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 | 0.000 |
| 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".